{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第一阶段 - 第一周 - 基础作业"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、包导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # 矩阵操作\n",
    "import pandas as pd # SQL数据处理\n",
    "\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "\n",
    "import matplotlib.pyplot as plt   #画图\n",
    "import seaborn as sns\n",
    "from numpy import array\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"day.csv\").drop(\"casual\", axis=1).drop(\"registered\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "               cnt  \n",
       "count   731.000000  \n",
       "mean   4504.348837  \n",
       "std    1937.211452  \n",
       "min      22.000000  \n",
       "25%    3152.000000  \n",
       "50%    4548.000000  \n",
       "75%    5956.000000  \n",
       "max    8714.000000  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 14 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(9), object(1)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从输出看出，由于dteday是object，是日期，其实和instant是一致的，所以在此干脆把dteday给移除掉。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "0        1       1   0     1        0        6           0           2   \n",
       "1        2       1   0     1        0        0           0           2   \n",
       "2        3       1   0     1        0        1           1           1   \n",
       "3        4       1   0     1        0        2           1           1   \n",
       "4        5       1   0     1        0        3           1           1   \n",
       "\n",
       "       temp     atemp       hum  windspeed   cnt  \n",
       "0  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2  0.196364  0.189405  0.437273   0.248309  1349  \n",
       "3  0.200000  0.212122  0.590435   0.160296  1562  \n",
       "4  0.226957  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(\"dteday\", axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 标签直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf98438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('cnt plt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集中在4000左右，和正态分布比较接近，8000左右也比较多，但似乎也是正常现象。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 特征单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed8b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd05160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfaf748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb09c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s6a4vv1ZL9o1X6SrF5Hs28Vn2dJUmp41WqoyM1Timp4uecv2z3DdeZaxSY1lvme6uEtVaMjxSoZbJiiVlyl0lxio1BkfGKZeCFUu6KUX9AprBkXGWdpcnL6LZvW+c4dEKxx7TzbFLyoxXk+f2jDIyXmPl8h5W9JbZO1Zl2+AImXDKcUtY1tPFc3vG6N81wjE9JU49/hi6u0o88/w+tu7ax6oVvZxx4lIqteTR7UNsHxzlzJVLOatvGTv3jLHp6d3s3jfOy049lrP7lvPwtiE2/Pw5ShG86oUrOePEpdz28A5ue3gHpxy3hDe/fDVLe7q49l+3cPOD23nN2Su5/DVnsrl/iP/93YfZvW+cl5yygo++cS2fvuWRA3Ll117cx8Pbh7js1Weye984N963FYBfXbuKiOCcU44leIZyV3TsbkUwv0A/DXhqyvOngVfNr5yZ3fzgNkoBLzmlHuhnrlzKsp4uKrXk4nWnsry3zFvPO5Wv3vUUy3vLXPu+X2btSSu4/Jof85++9hN6yiU+cuFaBkfGue5ft/DlDU8SAa9b28dPtw3yvms3Tq7r5GN7+db92/izb/+0qdoiIPPAaaWAWh663UxtyqWgMm1id1cwXj30tAjoigPnLZeCauYB6+zpKjFWPfBYQ2+5NBnAE3VN/OFOXVYp4oB5e8qlyYCcuqx6gO1fVnfXgcsvl+pf8KnTesolSA5Yfm+51AjInOxjfflJtTGt/g9ETIbaxHtTikMvP6L+XtQaQT1Ra8+05dcD9sDl14OZWZffWy5Rqe6vf2L5Y5X978/E8kfG9y9r4h+bscrhvdcTfWrms4zggO9PT1eJSq12wPdxpu/KTNOmfxcnsmvqsmb6Xs80bWLjYqqZ/k6mm+lv8FBWLuth9779By17yyV+dW0ftz28g5t+Ug/mC846kUvWncZfff8x3v+lew7Ilff+zY/5zoPbuGTdqbzklGOpZbLp6d38/Nk9vGx1PaOW95ZZs2oZzw2PNl9YC0QezjsxdcaItwO/mZn/sfH8PcD5mfnhae2uBK5sPD0HeHiOta4Cnp3jvIuVfT462Oejw3z6fGZm9s3WaD5b6E8Dp095/gJg6/RGmXk1cPU81gNARGzMzPXzXc5iYp+PDvb56NCJPs9nb/1dwNqIeGFE9ADvBG5qTVmSpMM15y30zKxExIeA7wJdwDWZ+WDLKpMkHZZ5XSmamd8CvtWiWmYz7902i5B9PjrY56ND2/s854OikqQjy6K59F+SdGhHXKDPNpxARPRGxNcar2+IiDWdr7K1mujzf46IhyJiU0TcGhFnLkSdrdTssBER8baIyIhY1GdENNPfiHhH43N+MCKu73SNrdbE9/qMiLgtIu5tfLffshB1tlJEXBMROyLigYO8HhHxl433ZFNEvLKlBWTmEfND/eDqz4CzgB7gJ8C509p8EPhc4/E7ga8tdN0d6POvA0sbjz9wNPS50W4FcAdwJ7B+oetu82e8FrgXOKHx/KSFrrsDfb4a+EDj8bnAloWuuwX9fh3wSuCBg7z+FuDbQAAXABtauf4jbQt9cjiBzBwDJoYTmOpi4LrG4xuAC2PiOuvFadY+Z+Ztmbm38fRO6uf8L2bNfM4A/xP4X8BIJ4trg2b6+7vAZzPzeYDM3NHhGlutmT4nMDHoyXHMcB3LYpOZdwCHugPPxcDfZt2dwPERsbpV6z/SAn2m4QROO1ibzKwAu4GVHamuPZrp81RXUP8XfjGbtc8R8Qrg9Mz8x04W1ibNfMYvBl4cEf8SEXdGxEUdq649munzJ4F3R8TT1M+W+zDFd7h/74flSLvBxUxb2tNPw2mmzWLSdH8i4t3AeuDX2lpR+x2yzxFRAj4DvLdTBbVZM59xmfpul9dT/x/YP0fEyzNzV5tra5dm+nwpcG1mXhURrwb+rtHnIt/coK35daRtoTcznMBkm4goU/+v2mK+yWhTQyhExBuBPwTempmdHfGn9Wbr8wrg5cDtEbGF+r7GmxbxgdFmv9c3ZuZ4Zv6c+phHaztUXzs00+crgK8DZOaPgCXUxzspsqb+3ufqSAv0ZoYTuAm4vPH4bcD3s3G0YZGatc+N3Q+fpx7mi33fKszS58zcnZmrMnNNZq6hftzgrZm5cebFHfGa+V7/A/WD30TEKuq7YB7vaJWt1UyfnwQuBIiIl1IP9AGK7SbgssbZLhcAuzOzv2VLX+ijwgc5CvwI9SPkf9iY9sfU/6Ch/qH/X+Ax4MfAWQtdcwf6/D1gO3Bf4+emha653X2e1vZ2FvFZLk1+xgF8mvr9BO4H3rnQNXegz+cC/0L9DJj7gN9Y6Jpb0OevAP3AOPWt8SuA9wPvn/I5f7bxntzf6u+1V4pKUkEcabtcJElzZKBLUkEY6JJUEAa6JBWEgS5JBWGga1GKiC2Ni60Od76MiBc1Hn8uIv6ombbSYnCkXfovdUxmvn+ha5BayS10SSoIA12L2brGTQJ2N256sgQgIn63cQOBnRFxU0ScOtPMEXFtRPzJlOf/NSL6I2JrRLxvWtvfatyIYTAinoqIT0557Z8i4sPT2m+KiEta2ltpFga6FrN3ABcBLwT+DfDeiHgD8GeN11YDT1Afi/uQGsPV/hfgTdQHxZq+f34PcBlwPPBbwAemBPZ1wLunLOs86kOiduoG6hJgoGtx+8vM3JqZO4H/B6wD/gNwTWbek/VRKf878OomblX4DuBvMvOBzNxDfazuSZl5e2ben5m1zNxEfcyOiWGMb6Q+ENXE6IjvoX5XqbH5d1FqnoGuxWzblMd7geXAqdS3ygHIzGHgOWa/icCpHHjjgSemvhgRr2rc/3IgInZTH3BpVWMdo9SHgX13Yyz3S4G/m1OPpHkw0FU0W4HJm2hHxDLqd7R6Zpb5+jlwnOozpr1+PfWhT0/PzOOAz3HgzQquo/6/gwuBvVkf31vqKANdRXM98DsRsS4ieoE/pX4j3i2zzPd16vvgz42IpcAnpr2+AtiZmSMRcT7wrqkvNgK8BlyFW+daIAa6CiUzbwX+CPgG9a3us6nfXGG2+b4N/AXwfepj7X9/WpMPAn8cEUPA/6Bxp51p/hb4JeBLc61fmg/HQ5daJCIuA67MzF9Z6Fp0dHILXWqBxm6aDwJXL3QtOnoZ6NI8RcRvUr8X5nbq+/ClBeEuF0kqCLfQJakgDHRJKggDXZIKwkCXpIIw0CWpIAx0SSqI/w86LCoi6aB7hAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10de6c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbcf70b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xca31630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4e2be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3804e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b32cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1117e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf98828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tdata = data.values.T\n",
    "columns = data.columns\n",
    "size = len(columns)\n",
    "fig = plt.figure()\n",
    "for i in range(size):\n",
    "    sns.distplot(tdata[i], bins=30, kde=True)\n",
    "    plt.xlabel(columns[i], fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图看，instant数据是一个线性递增数据，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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krSepc75b07Q4zfi3WPdhnHpyGbgJ/KaUeh2423dI1ZVSW5VSB4AeQLUs6/7WNC09Y356qZw3N6dp2mxN0+prmla/z9uWF+I8ba4nJuGUpZPq5FaMG2cypw7YFypAkcqlafnnCHx3TaFE3Yo08f/CdFHo41aqSCHOpGSOlJ9NuWoxbeWvnVF416kEQK3ybtxKM5By7YZp/do9MbStV+nxBM7mduJFHNwzRzsc3IqTejbJrIwh+Qra7TQAzi8KwanGczbPFR+fiKdn5pu4h4ebaUpCTmX0ej3OzoVJSrr7NJOLF5O5du06q1atBWDlyiBq165ug/SZ+dYFb+T69RtcvJjM1tCd1KxZFaUUCxYup76XN/W9vKlWvSljxv5Ix45tCQ8LJjwsmHp1axIfn0jpLHXg6eFmGl1LTDT+e/78RVatWoOXV+0HzxVnWbcJiWctypTOUrcuLs4kJSUTl9PPJeEsL71Un/btvYk5upNFC2fQosXLzPOfarbP9PR0li3/h9de83ngrBbZ48/g6elmnj0hW/Z7tAsPD1eWLp1Nnz6DOHHi1EPneFjnEs/zrPuzpufPupXkwtkHHzG8UzbhVCJ7duzj+eoVrZ4xO8vMJbhw9sI9tjCXNfPeHfuoZIPM+f18cTbxHG5ZRvpLuT/LuTMPXsd5If3CeXQlMtuFrnhJ0pMuWJS5vSsUDAbSz54hPf40Onfz68G0pIuknYrFvmrNx5LbljQt3aaPvPIkdc5vZfm/AbDTNC0NaAD8iXGe+dq7bOsPDNA0rQYwGihwl/3mNEb8/17Svv8oXN6VgqVLorPXU6bji8QFR5jWp165wcrq/QhoOJCAhgO5sOcYW3tPJmn/iXvs1XaqlSnFqfMpxF+8RGqagXV7jtKshvkHBbeihdh11HiB5X9nkridaqBoIUcA0tM1QvbG0DYPprQAXNsXwzPl3XAo/SzK3o5iHRuTHBxmVsb+2cwRjSLeXtw8Fpd9N1YXHh5JxYrlKVeuNPb29nTp4ktgYIhZmcDAENNUkddfb8emTdvvu9+goPU0a9YIgBYtXja7CNLa/glYR+OXG6LX63F0LECDBnWIjo5hw8ZQXn+tvekC0aJFi1CmjAerVq01ddgj9uwnIDCYrl074uDgQLlypalYsTy7w/bi5ORIoULGD4BOTo60btWMQ4eOPHCusPB9ZnXbrWtHAgODzcoEBgbTq1cXAN54w4eNm7aZlnfLIdOIEZMo/1x9Kj3/Ij16fsTGjdt4p/enAFSoUM603/Y+rTly5BgP6067KFs2s10EBZm3i6Cg9fTo8QZgbBebNxvbhYuLMytX+vHNN9+xY4flXZUeh8P7oild3gO30q7Y2dvRqmNLtgbfv90CFHYphL2DPQAuRZ2p6VWdE0dP3merRxe9LxrPLJlf6diS0OAdD7Rt9sw1vKoTa4PM+f18cXDvYco8VxqPMm7Y29vRrlNrNq7bYpNjWUtaTDR6d090pVzBzo5nmrYkdfc2szK3d4ZiV8N4TYpydkHnXpr0MwnoipeEjOs9VMFC2FepjiH+9GN/DeLBPNH3OVdKFQKcNE1brZTaCdx5h7kCZL2qsjCQqJSyxzhybn6bEUvZt39shoycRNje/aSkXOaVTj356P1evJHtwrDHTTOkEz7cn+Z/fInS6/hvyWYuH42nxpA3SIo8QXzwnntu77trCvaFHNE52OHZpj4bu0+yuNOLNdnpdQzr3Iz+M/4hPT2dji9WpaJbcWYE7aRqmWdpXuM5BnVqwpglG1i0cS8oxegerUxfsUYcj6dUkUJ4lnCxWcZ7MqRzasQcXvhjJOh0XFj6LzePnsZ9cHeuRx4jJSSMUu/5UMTbC81gIC3lKicG/mL7WAYDAwd+TUDAAvR6PfPmLeXw4aN8880gIiIOEBQUgr//UubOncKhQ1tISkrh7bcHmLY/cmQbhQsXxsHBHl/fNrRv35Po6BhGjJjI3LlT+P77kVy4kETfvl88dMaFC6bTrGkjSpQoRux/4Ywe8wP29saOyOw5C4iOPsa64I3s3bOe9PR05s5dbOpEfzPqO9asXoxOp0hNTePTT4dz6pR5O42KOsqKFQEciNxImsHAp58NJz09nVKlSrJi+e+A8av3JUv+Zl3wplzV7WcDRxAU9Ad6nQ7/eUuJijrKyJGDiYiIJDAwhLl+S/D3n8rhqFCSk1Po0fMjU6blKwLYny3T3SilmPv7FJydC4FSHNgfxcePMJ3EYDDw+effEBAwP6NdLOPw4Ri+/noQe/bsJyhofUa7+ImDBzeTnJxCr17GdtGv3ztUqFCOYcM+YdiwTwDw9e3F+fMXGT/+K7p164iTkyPHju3Ez28J48dPeeicd8+fzuQRU5nyx3fodDoCl67hxNFYPhj8LocjjxAasp0qtV5g0u9jKexSiMatG9Hni3fp0fJdylUqy5eTBpGuaeiUYsG0xWZ3TLEVgyGdn0b8wo9/fItepzdl7jO4N9GRRwkN2U7lWi8w8fcxFHYpxMutG9Hni970bPkeZSuVZeikz02ZF9ooc344X9wv//ivfmD2kqno9Dr+WhzA8SMnGDC0L4ciD7Nx3Vaq167Cz37f4VykMM29m/DxkA/o2Mz4bfr8VbMoX7EsTgUd+XdvAN98Po5tm3bZJKtJuoFrM6fgPPoH0Om4tX41hlOxOPZ4j7SYaFJ3byd1z27s63jhMn0epKdz3e9XtCuXsatdn8LvfQRogOLGX0sxnPzvfkd88j0hF3Fbm3qUq/gf+eBKXdU0rZBSqjkwWNO09hnLpwHhwDpgFcaRcAX8oGnaPKXUy8AcjKPinQFvYChwEjgAFNY0rbdSyh8I1DRtRbbj2WMchS8B+Oc07/yO1Av/PeGT0CytqPl1XkfIlU5+jfI6Qq4dfO/u99l+EjW5GHH/Qk+YtHz4xyXy21dzdvonenwmR7WL2X6KlzXp8l2rgIiLD/9NS16p4OJ2/0JPkC0NCty/0BOoeMDmJ6pB34rZbtM+2jOVXsqT15unZ2ZN0wpl/LsJ2JRl+YAsxSzuMadp2jbMb6X4a8Yje7nedzleKvDKQwcXQgghhBB5S+5zLoQQQgghhLCl/PedphBCCCGEEPlw+uODkJFzIYQQQgghnhAyci6EEEIIIfIfmXMuhBBCCCGEsCUZORdCCCGEEPnPU3qfcxk5F0IIIYQQ4gkhI+dCCCGEECL/kTnnQgghhBBCCFuSkXMhhBBCCJH/PKVzzqVzLoQQQggh8h1Nkz9CJIQQQgghhLAhGTkXQgghhBD5z1N6Qah0zp9CnfePzesIuebo3iSvI+TKlYUf5nWEXEnvFZbXEf5f0Ony15eRmqbldYRca2/nntcRckWPyusIuVbiWae8jpBrTip/dWfWRBTK6wgPpWdeB/h/In+15jywoubXeR0hV/Jjx1wIIYQQItee0gtC89cwjxBCCCGEEE8xGTkXQgghhBD5z1M651xGzoUQQgghhHhCyMi5EEIIIYTIf9LlPudCCCGEEEIIG5KRcyGEEEIIkf/InHMhhBBCCCGELcnIuRBCCCGEyH/kPudCCCGEEEIIW5KRcyGEEEIIkf/InHMhhBBCCCGELcnIuRBCCCGEyH9kzrkQQgghhBDClqRzbgNuzWvis/V72m+bTJUBvnctV9qnAd0TFlGsZnkAHIoWouXy4XSO+Z164995XHHva8SEH2nq8yadevZ77Mdu492cQwe3EB0VytAhH1usd3Bw4I9FvxIdFcr20ADKlvU0rfty6ACio0I5dHAL3q2bmZbPmT2ZhLhI9u3912xftWpVY9vWAMLDgtm5YzVe9Wtb9bVsi0mg45R/8P1pFXO3HLJYn5hyjT5z19Nt+mq6TAti69F4AA7EXaDr9NXGx7QgNkSdtmqurLy9m3PwwGaiokIZMjjn+l60cAZRUaGEbs2s72LFihC8bhlJF48wZco4s23GjB7K8WO7Sbp4xGo5rd0unnnmGXZsCyQiPITIfRsY+c0XpvIf9e9NdFQoabfjKV68qFXye7duzoH9m4g6tJXBgz/KMf/CBTOIOrSVrVv+MavndeuWcvFCNFN+Gmsq7+hYgL//8md/5Eb27lnPuLHDrJLzjtatm7F//0YOHdpy17wLFkzn0KEtbNmyKlveJVy4cJiffhpjtk3nzr6Eha1jz571jB//P6vmze65ZjXpt+F7+m+eTKP+lufkuj1e4YN1k+izegJvr/iGEpU8AHCv9Rx9Vk8wPtZM4IU29W2aM6vyzWrywYbv+XDzZF7MIXPtHi15b91E3l09nh4rvqZ4JXez9c7uxRkU9RsN+rZ7LHnrNKvLjI0zmbllNm981NlifYc+nZj27wx+XvcLYxaPp6RHSdO6kfNHs+jAEkb4ffNYst5Rq1kdJm+Yzk+bf6VD/9ct1rfr04Hv1//Ct2unMPyPMZTIyFzCoyTjAyczcfVPfB8ylVY92jy2zG7Na9Jh6/d03DaZavfoX5Tx8aJnwkKz/kWr5f+jW8xveI1/+3HFtb30dNs+8oh0zq1M6RT1JvRmU4/vWN18KGU7NsI540SflV3BAjz/fhsuRBwzLTPcTGX/98vZN+aPxxn5vjq1a83MH8fdv6CV6XQ6pv48nva+PalRqwXdunWiSpVKZmXee7c7ycmXqFy1MVOmzmHihOEAVKlSia5dO1Kzdkt82vfgl6kT0OmMzX3+/GX4tO9hcbxJE4YzdtyP1PfyZvToH5g0cbjVXoshPZ2JAWFMf7sFKz9pz9r9sRw/d8mszJzNB/GuXoalH7djUtfGTAgIA6Dis0X4o19bln3cjunvtGTsP7tIM1j/pKHT6fj553H4duhFrVot6NatI1Uqm9f3u+++SXLKJapWbczUqXOYkNGpunnzFqNGf8+Xw8Za7DcwaD0vN25v1ZzWbhe3bt2ilXdX6tVvTb363rTxbk7DBnUB2L4jjDavvklsrHU+FN2p5w4d36ZW7ZZ069qRytnrufebpKSkULVaE6b+8hvjx2XW8+jRPzBsmOXv409TZlGzVgsaNHyVRi950ca7uVXzduz4DrVrv0LXrh0s8vbu3Y2UlEtUq9aUX375jXHjvsqSdzLDho03K1+sWBEmTvwfr77anbp1W1GqVAlatHjZKnmzUzpF27G9WfLOd8xqNZRqHRqZOt93HFy1nTlthvFbu/+xY2YgrUYYzw/njsTxu+8Ifmv3P5a88x2vTngPpbf926bSKbzHvsOyd75jTquhVO3wokXnO2rVDua2+Qq/dsPZNTOIV0b0NFv/yjc9+G9TpM2zgrGNfDiuP6PfGcmAVz6iSYdmlK5U2qzMiUPHGeTzOZ+1+YTtQaH0/t+7pnV/zVrJlM9/fCxZ71A6He+O/ZBv3xnD4Faf8FKHJnhU8jQrE3voP4a3/4Iv2w5k1+rtvPWVcdAs+VwyI1//kq/afc6IjkPp0P8Nij5rnQ/u986saDDhHTb0+I6A5kMp1/FFXLK1CzD2L154vw3ns/UvIr9fwZ4nrH8hciadcysrVqcCV2PPcu3UedJTDZxatRPPNvUsytUc2pnDMwIx3LptWma4cYsLu49iuJX6OCPfV/3aNXBxLvzYj9vAqw7Hj8dy4sQpUlNTWbZsFR18zUcoOvh6s2DBcgD+/DOIli0aZyxvw7Jlq7h9+zaxsac5fjyWBl51ANgauouk5BSL42maRuGM1+nsUpiExLNWey0H4y5SunhhPIsVxt5OT5saZdl02Lyzp4BrN40/+6s3b1OysCMAjg522GV0CG6nGVAoq+XKysurtkV9+/p6m5XxzVrfK4NokVHf16/fYPv2MG7evGWx392793DmzDmr5bRVu7h27ToA9vZ22Nnbo2kaAPv2HeLkyTir5beo5+X/5FzPC1cAsHJlkKnjaqrnW+b1fOPGTTZv3gFAamoq+/YewMPTzSZ5ly8PyDHvQlPe1RZ5b926aVa+fPkyxMSc4MKFJAA2bAilU6dXrZI3O/faFUiKPUvKaeM5OSpgJ8+3Nj8n3756w/R/e6dnTP9Pu3kbLeODsP4ZezKahM251a5AcuxZLmXJXOk+mTUyw1XyrkfKqfNcyPj2zdYq1X6eM7GJnD11lrTUNLYGbKGB94tmZQ7sOMDtjPPDkb1HKO5WwrQ8TR0GAAAgAElEQVRu/7ZIbmR5PY9DxdqVOBObyLnTZzGkprEjIJT6rRualYnacZDbN43v0cf2HqGYW3EADKlppN1OA8DewR6ls805ObvidSpwJfYsVzP6F7F36V/UGtqZqBmBpGfpSxhu3OL8E9i/eFSaZrDpI6/kaedcKVVQKRWklIpUSh1USnVTStVTSm1WSkUopdYppdwyyn6glArLKPunUsopY3mXjG0jlVJbMpYVUEr5KaUOKKX2KqVaZCzvrZRaqZRaq5SKUUp9Z+3X5ORajOsJF03Prycm4ehm/om6aPWyOLkXJ2H9Xmsf/qni7uHK6bgE0/O4+ETc3V3vWsZgMHDp0mWKFy+Ku3sO23qYb5vdoMEj+XbiCE4cD+O7SV8zfMREq72Wc5dv4OriZHpeysWJc1fM34z6taxJUOQJvL9fyYAFmxjmk/kV+oHTF3h9aiCdpwUxokMDU2fdmjzc3Yg7nWh6Hh9/BncPt2xlXImLM5YxGAxcunzZalM9HpSt2oVOpyM8LJjE+P38++8WdofZ5vcze4b4+EQ8sud3dyUuS/7Ll688cD27uDjj49OKjRu3WS1vXLa87u6lHinv8eMnef75CpQt64ler8fX1xtPT8sRQGso7FqMK4mZ5+TLiUkUdrXMVu/t1ny05Ude+ao760bOMy13r12BviHf0nfdJNYOn2vqrNtSYdeiXElMMj2/cpfMdd9uxYdbJtPiqzdZP3I+APaOz/Bi//aETllp85x3FHctzoWE86bnFxMvULxU8buWb93Nm4iNEY8j2l0VdS3GxcQLpucXEy9S1LXYXcs379aKyE17TM+LuZXg27VTmLbzN/6ZuZLkc8k2zQvg5FqU6wmZ7eJ6YhJOOfQvCroXI379PpvneSLItBabaAskaJpWS9O06sBa4Begs6Zp9YC5wJ3vQ1dqmualaVot4DDwfsbyb4A2Gcs7ZCz7GEDTtBpAd2CeUqpAxrraQDegBtBNKWX+3RuglOqrlApXSoX/e/1Y9tX3ltMH6KzDLUpRZ1RP9o5elLv9/j+klGVlatmGrnIu82DbZvdh37f5Ysgoylfw4osho5kza3IuE99d1lGtO7InXLs/lg51KxA85HWm9WrOiD+3k55u3K5G6RKs/LQ9iz5sy+9bDnEr1fqf6HOosges78c0nJiLDA/TLtLT06nv5U3Z8vXxql+HatVesFJicw+W33K7B6lnvV7PgvnTmD7djxMnTj10RvMsD1vfd8+bknKJTz8dzoIF0/n33xWcPBlHWlrao4d9QDlli5gfwoymg9gwaQmNP+lkWp6w7zizW3/J3A5f89JHHdA/Y/8YEubUACwX7Zm/nllNv2DTpCW8lJG58aDXCfttLanXLb/FsplctNdmrzWnYs2K/DXrTxuHurccv4G8S5Nt/FoznqtRkYBZf5mWJSVe4Mu2A/m8aT+avtEClxIuNkqaxV3Oa1nX1x/Vk4jRMnUlv8vrzvkBoJVS6lulVBOgNFAdCFFK7QNGAHcmgVVXSm1VSh0AegDVMpZvA/yVUh8A+oxljYEFAJqmRQMngecz1v2radolTdNuAlFA2eyhNE2brWlafU3T6r/iVDFXL+h6YhJO7pkjBk5uxbhxJnMKhX2hAhSpXJqWf47Ad9cUStStSBP/L0wXbYhM8XGJlM4ymubp4UZitqkmWcvo9XpcXJxJSkomPj6HbRPuPU3l7V5d+Ouv1QCsWBGAl5f1Lggt5ezEmUvXTc/PXrpumrZyx18Rx/GuXgaAWmVKcistnZRsb7DPPeuCo4Mdx85ZTst5VHHxiXiWzhwp9/BwJTHhjGWZjOkSer0eF2dnkpKsn+VebN0uLl26zOYt2602Z9sif7YMHh5uFlOo4uPPmEaS9Xo9zs6FH6ieZ8z4lmPHTvDLtN+tmtczW97ExHN3LfOgeVevXk/Tph1p3vw1YmL+49ixWKtlzurKmSQKu2Wek53dinH17N2zHfpnB897W174efFYArdv3OLZ5z1z2Mq6jJkzR3ELuxXjytm7j8xG/bOTSt7G6Q3utSvS4qs36R/6E/Xfa0OjjztQ953WNs17MfEiJdwzL/As7laCpHNJFuVqNa5FlwHdGP/+WNO0kLySdOai2dSa4m7FST5rmbn6yzXpNKAzP/SZkGPm5HPJxB09zQsNqto0L9zpX2S2C2P/IrNd2BcqgEtlT1r/OZxOu36iRN0KNPcf9HT3L7R02z7ySJ52zjVNOwrUw9hJnwi8ARzSNK12xqOGpml3Jjf6AwMyRsNHAwUy9tEPYye+NLBPKVWcnMev78ja2zFg5Xu9J+37j8LlXSlYuiQ6ez1lOr5IXHDm13epV26wsno/AhoOJKDhQC7sOcbW3pNJ2n/CmjGeCmHh+6hYsTzlypXG3t6erl07EhAYbFYmIDCYXr26APDGGz5s3LTNtLxr1444ODhQrlxpKlYsf99pCgmJZ2nWtBEALVs0JuaY9X4m1TyKc+riFeKTr5KaZmDdgZM0q2z+Ju9WxIldx42d4f/OXeJ2moGiBZ8hPvmq6QLQhJSrnLxwGfciBa2W7Y7w8EiL+g4MDDErExgYklnfr/uwaZN1pk7khi3aRYkSxXBxcQagQIECvNKyCUeOHLdJfmM9l8vM36VDzvXc03jHi9cfsJ5HjRqCi3Nhvhg8ygZ5M+u7SxffHPP2NOVtx6ZN2++735IljR3mIkVc6Nu3F35+i62a+46EyP8oVt4Vl4xzclXfFzkaYj6lomi5zGk6lVrWJjnW+HvoUrqk6QJQZ48SFH/OjZS489haYg6Zj4XsMSuTNXPFLJkXdRnLr40/59fGnxM+dx07pv/DnnnmPy9ri4k8ilt5d54tXQo7ezua+DZld8guszLlqz1H/4kDGP/+WC5dvHSXPT0+xyNjcC3vRsnSz6K3t6ORb2MiQnablSlXrTx9Jn7ED+9P4HKWzMVci2P/jAMABZ0L8kL9yiQeT8DWLmbrX5Tr+CJxwZntIvXKDVZU78/fDT/n74afc2HPcTb1/lH6F/lQnv4RIqWUO5CkadpCpdRVoC9QUinVSNO0HUope+B5TdMOAYWBxIxlPYD4jH1U0DRtF7BLKeWLsZO+JaPMBqXU80AZ4AhQ19avSTOkEz7cn+Z/fInS6/hvyWYuH42nxpA3SIo8QXzwnntu77trCvaFHNE52OHZpj4bu0/icszjuajnboaMnETY3v2kpFzmlU49+ej9Xrzha/tbRxkMBj4bOILVQX+g1+nwn7eUqKijjBo5mPCISAIDQ5jrt4R5/lOJjgolOTmFt3oab/MWFXWUFSsCOBC5kTSDgU8/G056xvyxhQum06xpI0qUKEbsf+GMHvMDfv5L6NdvCD/+OAY7Oztu3bxJ//5DrfZa7PQ6hrWvT/95G0hP1+hYtwIVSxVhxr+RVHUvTvMqngxqW48xq3ayaHs0KMXo1xuhlGLvyXPM3RKFnV6HTsFX7b0oWrDA/Q+aSwaDgYEDvyYocBE6vY55/kuJOnyUkd8MJmKPsb79/Jbg7/czUVGhJCel0LNX5m31jh7ZgbNzYRwc7Ong2wYfn7c4HB3DxAnD6datE05Ojvx3PAw/v8WMHffwd2awRbtwcyvF3N+noNfr0Ol0rFgRQNDq9QAM+Pg9Bn/xEa6uJdkbsZ41azfwYb8hj1zPgQEL0ev1+M9byuHDR/nmmy/YE7GfwKAQ/PyX4Dd3ClGHtpKUlEKvtzNvF3nkyHacCxvr2de3DT7te3DlyhW+GvYp0dEx7Nq5BoBfZ/rj57fkoXNmzxsQsAC9Xs88U95BREQcICgoBH//pcydO4VDh7aQlJTC228PyJJ3G4Wz5G3fvifR0TFMnjyKGjWMo40TJkzhmBU/DGelGdJZ940/3ed/iU6vI3LZZi7ExNN00Bsk7j9BzPo91H/Hm/KNq5OeauDG5Wv8M2gmAKXrv8BLH/mSnmpA09JZO8KPG8lXbZIze+bgb+bRbf5QlF7H/ozMTTIyH1u/h3rveFO2cTXSUw3cvHyNoEGzbJ7rbtIN6cz+eiajFoxBp9fx79IQTh89xVuDenDsQAy7Q3bz7vD3cHQqwNBfjbf5vJBwnvHvG+/uNGHFt3hW8KRAwQL8vsufaUOmsnfLvd8rrZHZ/5s5fDV/JDq9nk3L1hMXc5rOg7pzYv8xItaH8db/elPAqQCfzTC+F1xMOM8PfSbgUdGTniPeRdM0lFIEzl7F6SMnbZoXjO0ibPg8XvnD2C6OL9nMpaPx1MzoX8Tdp3/RaddPZv2LDd0ncSnG9h8qbOop/SNE6nHPFzU7uFJtgO+BdCAV6A+kAVMBF4wfHqZomjZHKdUfGIpxisoBoLCmab2VUiuBShhHy/8FBgLPADMxjsqnAYM0TduolOoN1Nc0bUDG8QOBHzRN23S3jIvde+RdBT2Ezvstb2WXHzi6N8nrCLlyZeGHeR0hV1x6zc7rCLmWnofnpoel1+X1TMHcsdWdf2zp61L561yhz4d1vEN7vFPVrMFJ5a8/eO6TWiivIzyUngkLn6gGfePf2TZ9o3B8pW+evN48bc2apq0D1uWwqmkOZX8Ffs1hueVfDoCbQO8cyvpjnB5z57n1br4shBBCCCEenzycF25L+WuYRwghhBBCiKdY/voeSAghhBBCCHhq55zLyLkQQgghhBBPCBk5F0IIIYQQ+Y/MORdCCCGEEELYkoycCyGEEEKI/EfmnAshhBBCCCFsSUbOhRBCCCFE/iMj50IIIYQQQghbkpFzIYQQQgiR/8jdWoQQQgghhBC2JCPnQgghhBAi/3lK55xL5/w+Ovk1yusI/y/cSNia1xFyzdG9SV5HeGA6pfI6Qq7lv8RgyGdvFEUKFMzrCLn2U3JYXkfIlXRNy+sIuebmVCyvI+TasUsJeR0hVyZVfSGvI4gnmHTOnzL5qcN4R37smAshhBAij8mccyGEEEIIIYQtyci5EEIIIYTIf/LZVMIHJZ1zIYQQQgiR/8i0FiGEEEIIIYQtyci5EEIIIYTIf57SaS0yci6EEEIIIcQTQkbOhRBCCCFE/iMj50IIIYQQQghbkpFzIYQQQgiR/+TDv8D7IGTkXAghhBBCiCeEjJwLIYQQQoj8R+acCyGEEEIIIWxJRs6FEEIIIUT+IyPn4kFtizpJx3EL8B0zn7kh4RbrE5Ou0GfqSrp9u5guk/5g66FYAILCjtD128WmR53PfiE67rzVcrXxbs6hg1uIjgpl6JCPLdY7ODjwx6JfiY4KZXtoAGXLeprWfTl0ANFRoRw6uAXv1s1My+fMnkxCXCT79v5rtq9ataqxbWsA4WHB7NyxGq/6ta32Oh7EiAk/0tTnTTr17PdYjwv5v569vZtz8MBmoqJCGTI45/yLFs4gKiqU0K3m+YcO+ZioqFAOHthM6yz5P/20D/v2/svePetZMH8azzzzzKNnPLiFw1GhDLlLHS9a9CuHo0LZlq2Ohw4dwOGoUA4e3GKWMeboTvbuWW+qyzu+/noQsSfCCQ8LJjwsmLZtWz5SdrB+G/H0dGd98HIO7N9E5L4NfDLg/UfOmFXLVk3YGbGW3ftC+PTzvjnktec3vyns3hfCug3LKV3Gw2y9h6cbsQl7+fiT90zL+vZ/m607AwndFcSHH71j1by2ytzv496E7gpi685AZs/9kWeecbBq5ldaNWHXnnWE71vPZ4NyyuzA7/5TCN+3npANK3LMfCpxHwM+zfz5O7sUxn/BL+yMWMvO8LV4NbDNubhxixcJ3LaMNTtX0OeTty3W13uxNstD5hEZvw3v9ua/Q7MWT2HH0fVMXzjZJtmy8m7dnAP7NxF1aCuDB39ksd7BwYGFC2YQdWgrW7f8Y/rdK1asCOvWLeXihWim/DTWVN7RsQB//+XP/siN7N2znnFjh9k0f4GXvHBf6Yf7qnk4937TYn1BX288/12B2+KZuC2eSaFOrwLwTP1apmVui2dSZsdqHJu/ZNOs4uFJ59zKDOnpTFy+ien9OrDyfz1YG3GU44lJZmXmBIfhXacSS7/szqR32jJh+SYAfLxeYNmX3Vn2ZXfG92qNezFnKnuWtEounU7H1J/H0963JzVqtaBbt05UqVLJrMx773YnOfkSlas2ZsrUOUycMByAKlUq0bVrR2rWbolP+x78MnUCOp2x6cyfvwyf9j0sjjdpwnDGjvuR+l7ejB79A5MmDrfK63hQndq1ZuaP4x7rMSH/17NOp+Pnn8fh26EXtWq1oFu3jlSpbJ7/3XffJDnlElWrNmbq1DlMGP8/Y/7Kxvy1a7ekvW9Ppk4dj06nw93dlY8/fo8XG/lQp24r9Ho9Xbt2eKSMU38ej69vT2rWasGbd6njlORLVKnamJ+nzmFCljru1rUjtWq3pH22OgZo1boL9b28ebFRO7P9/Tx1DvW9vKnv5c3atRseOnvW/NZsI2lpaQwZOpoaNZvzcmNf+vfvbbHPR8n77eSRdHvjA172asfrndvz/AsVzMr0eLsLKSmXaFC7NTOn+zNy9BCz9eMm/o9/Q7aYnleuUole73TFu0Vnmr3UAe82LXiuQlmr5LVVZle3UnzwYS9aNXudJi+2R6fT8dobPlbN/N3kUXR9vQ+NvF7ljc7teeGFimZler7dmZSUy9Sv3Ypfp/sxaox55gmThptlBpj43Qj+Xb+FF+u1pUkjX44cOW61zFmzD580hH5vDaRDkzdp95o3FZ4vb1YmMf4swz8bS9DKYIvt585YyFcDRlk9V045f/55HB06vk2t2i3p1rUjlbOf33q/SUpKClWrNWHqL78xfpzx/Hbz5i1Gj/6BYcMs31d+mjKLmrVa0KDhqzR6yYs23s1t9QIo9uUnnPvkfyS88T4F27bAvnwZi2LXgjeR2L0fid37cfXvNQDcCo80LTv74RDSb97k5s4I2+R8nLR02z7yyP/rzrlSSm/tfR48eZbSJYvgWcIFezs9beo+z6YD/5kfF7h28zYAV2/eoqRzQYv9rIk4Stt6z1stVwOvOhw/HsuJE6dITU1l2bJVdPBtY1amg683CxYsB+DPP4No2aJxxvI2LFu2itu3bxMbe5rjx2Np4FUHgK2hu0hKTrE4nqZpFHYuDBhHbhISz1rttTyI+rVr4JJx/Mcpv9ezl1dti/y+vt5mZXyz5l8ZRIuM/L6+3hb5vbyMo3R2ejscHQug1+txdHIk8RFyZq/jpctW4Zutjn3vUse+vm1Yepc6flxs0UbOnDnH3n0HAbh69RrR0TF4uLtaJW/d+jU58d9JTsaeJjU1lb/+DOJVn1ZmZV71eYUli/8C4J+/19KkeaMs61pxMvY0R6KPmZY9/0IFIsIiuXHjJgaDge3bduPTvrVV8toqM4CdnR0FMtqxk5MjZ86cs1rmetkyr/wziFfbv2JWpp1PK5b8sRKAVX+vpWmWzO3atyI29jTRh2NMywoXLsRLL3mxYJ6xLaWmpnL50hWrZb6jRt2qnD4RR9zJBFJT01j9dwgt2jY1K5NwOpGjUcfQcpiGsGtrONeuXrd6ruwszm/L/8n5/LZwBQArVwbRosXLAFy/foPt28O4eeuWWfkbN26yefMOwFi/+/YewMPTzSb5Haq/QFpcAmnxiZCWxrV1m3Bs/nKu9+PUqik3t4Wh3bx1/8IiTzzVnXOl1Fil1GdZno9XSn2qlNqolPoDOGDtY55LuYZrkUKm56WKFOLcpatmZfq92pCg8CN4fz2XATMDGNa5WfbdELwnhlfrWq9z7u7hyum4BNPzuPhE3LO9eWctYzAYuHTpMsWLF8XdPYdtPe79xj9o8Ei+nTiCE8fD+G7S1wwfMdFqr+VJlt/r2cPdjbjTiabn8fFncPdwy1bGlbi4xMz8lzPye7iZlgPEx53Bw92NhIQz/DRlFseP7eLUyT1cvnSF9evNR/dyw93Dlbgs9RQfn2jREb1bHRuzm297p441TWPN6sXs2rmGPu+bf0vxUf932RMRwpzZkylSxOWhs2fPBtZvI2XLelK7VnV27d77SDnvcHMrRULcGdPzhIQzuLmXsigTn6VNXL58hWLFiuLk5Minn3/A95OmmZU/HBVDo5frU7RYERwdC9DKuxnuVuzQ2CLzmcSzTP/ld/Yd2sShmG1cvnyFTRu2WTGzK/Hxmb8/CfFncHPLltm9FPEZr8tgMHD50lWKFTdm/uzzvnw38Rez8mXLlebChSSmzfyWTaGr+HnaeJycHK2W+Y5Srs+SmJD5gftswjlKuVrnW19ryv77k+O5I8s54k67KF686APt38XFGR+fVmzcaL12kZVdyRKkZflAaDh3Hv2zxS3KObVsgtvS2ZT47hv0pSx/DgXbNOfaukf7BvCJkZ5u20ceeao758DvwDsASikd8CYQDzQAhmuaVtXaB9SwvCG+Usrs+dqIo3RoWJngse8xrZ8vIxYEk56eud2B2DMUcLCnorvlL93Dyp4BjJ2R+5d5sG2z+7Dv23wxZBTlK3jxxZDRzJll+7mET4L8Xs85RHjA/Npdty1SxAXf9t48/0IjyparR8GCjrzV/fVHyGibOm7WvBMNGralvW9P+vfvTePGDQGYNWs+L1R+iXr1vUk8c47vv/vmobPbMj9AwYJOLFs6h0GDR3LlylWLsg/jofOi8eX/PmXmdH+uXTMfFY05epypP83hz7/9WLbydw4diMaQlmaVvLbK7FLEmVfbvUK9Gi2p/nxjnJyc6NLt4adnWWa2XPagv3vDhn/Kr9P8LDLb2empVbsafr/9QfPGHbl+7QYDB31otcyZwSwX5fRemNcerF1Ybne/8zCAXq9nwfxpTJ/ux4kTpx464z3lGM786Y0tO4lv35PEbn25uWsPJcYMNc9Zohj2FctzY4fl9XDiyfFUd841TYsFLiql6gDewF7gIrBb07QTd9tOKdVXKRWulAr/fXXuPgGXKlKIMymZb4pnU65aTFv5a2cU3nWM89xqlXfjVpqBlGs3TOvX7omhbT3rzBe9Iz4ukdKe7qbnnh5uFlMLspbR6/W4uDiTlJRMfHwO2ybce1rC27268NdfxovqVqwIME1veNrl93qOi0/Es3TmCKaHhyuJCWcsy2SMcur1elycnUlKSiE+LnM5gIenKwmJZ3ilZWNiY09z4UISaWlp/P33Gl5sVO+hMxqPk1lPHh5uFtN57lbHxuzm296p4zs/p/PnL/L3qjWmujx37gLp6elomsbvvy+i/iPWsa3aiJ2dHcuXzmHx4r/4O2OeqTUkJJzB3TNzdNHd3ZUziecsynhkaRPOzoVJTkqhbv1ajBwzhD0HNvBh/3cYOLgf7/ftCcCiBSto2fQ1fF/tQXLyJY4fP/lEZ27W/CVOnozj4sVk0tLSCAwIxquh9aZEJSScwSPLt1TuHq4W02YS4s/gkfG69Ho9zi6FSE5KoV79WowaO5R9BzfS76PefP5FP/r07UlC/BkS4s8QER4JwKpVa6lZu5rVMt9xNvGc2TcTpdyf5dyZC1Y/zqPK/vuT47kj/ozpHHGnXSQlWU4pzG7GjG85duwEv0z73bqhs0g7dx4712dNz/XPlsRw/qJZmfRLlyE1FYCrf63GobL5N/BOrZtxfeM2SDPYLOdjpWm2fTwApVRbpdQRpdQxpZTFFcFKqTIZMzb2KqX2K6Xa5bSfrJ7qznmG34DewLvA3Ixl1+61gaZpszVNq69pWv332+VuPle1MqU4dT6F+IuXSE0zsG7PUZrVML8wxq1oIXYdjQPgvzNJ3E41ULSQ8avG9HSNkL0xtLXilBaAsPB9VKxYnnLlSmNvb0/Xrh0JCDS/MCcgMJhevboA8MYbPmzctM20vGvXjjg4OFCuXGkqVizP7rB7f2WekHiWZk2N8yFbtmhMzLG7fhZ6quT3eg4Pj7TIHxgYYlYmMDAkM//rPmzKyB8YGGKRPyxsH6dOJ9CwYR0cHQsA0KJFY6KzzeXNjex13K1rRwKz1XHgXeo4MDCYbjnUsZOTI4UKGT9EOzk50rpVMw4dOgKAa5Y3w04dXzUtt1Z+a7WRObMnczj6GFN+nv1I+bLbG3GA554rR5myntjb2/PaGz6sXW1+16C1qzfwZvfXAOjQqS1bM+bg+rZ9i7o1WlK3Rktm/TqPKT/M5PfZCwEoUaIYYLzDSPsO3qxcEfhEZ46LS6C+V21TO27arBFHj5hfT/Qo9kQc4LkKmZlff8OHtUHmmdes/pc33zJ+69SxU1u2bt4JgE+bt6hdvQW1q7dg5gx/fpo8k99mL+TcuQvExydSsZLxPahZs0YW8+it4eDew5R5rjQeZdywt7ejXafWbFz38FPXbMV4fiuX+bvXpUPO57eenQF4Pcv57V5GjRqCi3Nhvhg8yhaxTW4fOoJdaQ/s3F3Bzo6CbZpzY/N2szL6jN8rAMdmjUiNNR/FL9i2Jdce8aJ2kSnj2sXpwKtAVaC7Uir7rIwRwDJN0+pgnMEx4377/f9wn/O/gDGAPfAW0MSWB7PT6xjWuRn9Z/xDeno6HV+sSkW34swI2knVMs/SvMZzDOrUhDFLNrBo415QitE9Wpm+bos4Hk+pIoXwLPFo81qzMxgMfDZwBKuD/kCv0+E/bylRUUcZNXIw4RGRBAaGMNdvCfP8pxIdFUpycgpv9TTeZioq6igrVgRwIHIjaQYDn342nPSMuVgLF0ynWdNGlChRjNj/whk95gf8/JfQr98QfvxxDHZ2dty6eZP+/YfeK57VDRk5ibC9+0lJucwrnXry0fu9eCPbRXe2kN/r2WAwMHDg1wQFLkKn1zHPfylRh48y8pvBROwx5vfzW4K/389ERYWSnJRCz14Z+Q8b80dGbsCQZuCzz0aQnp5OWNheVq5cze5da0lLS2PfvkP89tuiR67joGx1PHLkYCKy1LG//1QOZ9Rxjyx1vHxFAPuz1XGpUiVZsdw44qW307Nkyd8EB28CYNLEEdSqVRVN04g9GcdHH335yHVs7Tby8kte9BoVAWkAACAASURBVOrZmf0HoggPM3b0v/56Emus8CZsMBgYNmQMy//6HZ1ezx8LVnAk+hjDhn/Kvj0HWbtmA4vmL2fG7O/ZvS+ElORLfPDu5/fdr9/CaRQrVoTU1DSGfjGaSymXHzmrLTPvCd9PwKp1bNj6N2lpaRzYf5j5fkusmnno4NGs+Hsuep2eRQtWEB19jK+Gf8bevQdYu3oDC+cvZ+acHwjft57k5BT6PEA9fzl4LLN+m4yDgz2xsacZ0N/6t/ozGAyM/+oHZi+Zik6v46/FARw/coIBQ/tyKPIwG9dtpXrtKvzs9x3ORQrT3LsJHw/5gI7NugMwf9Usylcsi1NBR/7dG8A3n49j26ZdNsk5cODXBAYsRK/X4z9vKYcPH+Wbb75gT8R+AoNC8PNfgt/cKUQd2kpSUgq93s681emRI9txLlwYBwd7fH3b4NO+B1euXOGrYZ8SHR3Drp3Gb6x+nemPnxXbRuYLSCfp2194dvok0Om4+s9aUv87iUu/d7gddZQbW3ZQ+M3XcGzWCAwG0i9d4cLI70yb691KoS9VklsR+62fLa/k/X3OGwDHNE37D0AptQToCERlKaMBzhn/dwESuA/1IHOp8jul1EwgRdO0YUqp5sBgTdPaP8i2N9ZNy1cVVNg3/114eeP/2LvvqCiuv4/j78sCtthjpNliib2CJWLBgg3EWDBFE01MoonRWBOjsSSWNDVqmprYTdTYpViwIVawItgVlWYFzC82WOb5Y3FlKSqymxWf7+scjuzMndkP17szd+/emY3dZe0IT6WAk0Xf55mVTVZzFZ9xefHYlNcSF8uf+U5RwrxS82A7dixY4vGFnjFnkx7b33mmnK7+irUjPJVyh4KeqZPJnfkjLfoCK/ju9x8C6b90YI6macaPJ5VS3YH2mqb1S3vcG2ikadrAdGUcgc1AcaAQ0EbTtEfex/K5HzlPuxC0MdADQNO0HcAOK0YSQgghhBC5ZeGR87SO+KPmCmb1ZiXjG4Y3gAWapk1VSjUBFiulampa9jdSf64752nzfvyANZqmnXlceSGEEEIIkUdY8YuC0kQDZdI9diHztJX3gPYAmqbtVUrlB14Esv2ihOf6glBN0yI1TXtZ07Rh1s4ihBBCCCGeK6FAZaVUBaWUPYYLPtdnKHMJaA2glKoG5AeuPWqnz/XIuRBCCCGEeD5pqda9pkPTtBSl1EBgE6AD5mmaFqGU+goI0zRtPTAMmKuUGoJhyksf7TEXVUnnXAghhBBCiKegaVoAEJBh2dh0v0cCObovt3TOhRBCCCFE3mP9WylaxHM951wIIYQQQoi8REbOhRBCCCFE3mP9u7VYhIycCyGEEEII8YyQkXMhhBBCCJH3WPluLZYiI+dCCCGEEEI8I2TkXAghhBBC5D1ytxYhhBBCCCGEJcnIuRBCCCGEyHue05Fz6Zw/xvF3t1o7Qo78s+RDa0f4f+NO7C5rR8iRwi4trR0hR1LJgwfdR38j8zMnNY/lBWhdorq1I+SILcraEXIshbzXLqrke8naEXJk6s2C1o7wVGZaO8D/E9I5F1ZXwKmZtSPkWF7rmAshhBDPnTw4wPAkZM65EEIIIYQQzwgZORdCCCGEEHnPczrnXEbOhRBCCCGEeEbIyLkQQgghhMh75BtChRBCCCGEEJYkI+dCCCGEECLv0Z7POefSORdCCCGEEHmPTGsRQgghhBBCWJKMnAshhBBCiDxHk1spCiGEEEIIISxJRs6FEEIIIUTeI3POhRBCCCGEEJYkI+dCCCGEECLveU5vpSgj5xZWpGU9agb/RK2QX3D4uGum9SV9Pah7bAE1Nk+jxuZpvPhGGyukhN1nYvH5cT3e09cxLzgi0/q4xH/pNy+Inj8H0OMnf3adjgEgPPo6vj8HGH5+8mdb5GWz5mrn2ZKI48GcjAxh5IiPM623t7fnz6W/cjIyhD0hGyhXzsW47rORAzkZGULE8WA827YwLp87Zyqx0Uc5cniryb7q1KnB7l0bCAvdzL69Abi51jXr3/IoYyZPo3mn1+nSq/9/9pxZadu2BceObSciIpjhwz/KtN7e3p7Fi38mIiKY4OB1xvouUaIYmzYt4/r1E0yf/pXJNt27exMauolDh4KYNOkLs+T09GzJ8fCdREaGMGJ41u1i6ZJfiIwMIWSXabsYOeJjIiNDOB6+k7bp2sXAge9x+FAQRw5v5ZNP3jMu/3LMUC6cDyP0wCZCD2yifftWT5/5eDAnIkMYkU1bXrr0V05EhrA7Q1seOXIgJyJDOH482CTzmdP7OHwoyNhmH6hduzq7gtdz+FAQa9YsoHDhF54qc3qt2zRj/6FNhB0JYvDQD7LM/8eCHwk7EsSWbSspU9bZZL2ziyOX4o4wcNB7JsttbGzYEbKOv/6ek+uM2anboh4ztv3CrJ2/0WVAt0zrvfp1ZnrQT/ywcQZj//yKF51LAVC+egUmrfmWaVtm8cPGGbzq5W6xjBnVaVGPqdt+ZvrOX+k8IPO5o2O/znwfNItvN/7I6HSZX3QuxSS/qUwJmM73W2bS5q12/0nevFjH9VrU56ftv/JL8Gy6ftQ90/rO/XyYufVnpm+ayYS/JlIqLTPAl4vGsyT8L0bPH/uf5QWo1qIOo7dO58sdM2gzwCfTeo/3OvHFlql8FvgdHy8dQ3HnF43rOn/+FqM2/8AXQdPoNq7Pf5ha5NRz3TlXShVTSn2U7nFLpZTffxbAxoZykz7gTK+vOe4xiJJd3Mlf2SVTsZvrdxPhOZQIz6Fc/yvoP4v3gD41lSkbQvn5bQ9Wf+LFxmNRnLuaZFJm7s7jeNYsy/KPO/KNrzuTN4QCUOmlYvzZvz0rPu7Iz++04uv1+0nRm+edrI2NDTNnTMLLuxe16njQs2cXqlWrbFLm3b5vkJCQRNXq7vw4cy5TJo8GoFq1yvj6+lC7bis6eb3FrJmTsbExNPdFi1bQyeutTM/3zeTRfD1xGq5unkyY8APfTBltlr/jSXTp2Jbfpk38z54vKzY2NsyYMREfn3eoW7c1vr6dqVrVtL779OlJYmISNWo0Z9as35k4cRQAd+/eY8KEqXz++SST8iVKFGPKlC/o0OEN6tdvQ+nSL+Lh0dQsOb0796ZOHQ969vShWoacffu+TkJiEtWruzNz5lwmp70pqFbV0C7q1m2Fl3cvZs6chI2NDTWqv8J7777Bq029aODqSceObahUqYJxfzNnzcWtYTvcGrZj48ZtT5V55oxJeHv3onYdD17Ppi0nJiRRrbo7M2bOZXK6ttzT14c6dVvhlaEtA7Rp2wNXN08aN+loXDb7t+/5YvRk6tVvw7q1gQwbNiDHmTPm/27qeHy79qOJWwe6dffilVcqmZTp9XZ3EhNv4Vq3Db/+PJ/xX40wWT/5m9Fs3RKcad/9P3qH06fO5Srf47K/9/WHTHpnAkPaDKRp52a4VC5jUuZCxAU+8xrK8PaD2Rewh96j+gBw7849Zg35kaFtP2HS2xPoM+49ChYpZLGsDygbG/p+/SHfvvMVw9t8wqudm+Gc4dwRFXGe0V7D+Kz9p+wP2MObo94BIOFqAuO6fsaojkMY4zOSzgO6Ufyl4hbNmxfr2MbGhg8m9ufrd8YzqPXHuHduninz+YjzDO80lCHtBrHHfzdvf9HXuG7t7NX8OGSaxXOmp2wUPb56l9/6TGFy26E06NwUh0qmb4KjI6P43nsU33YYydHA/fiMMpzrKtSvwsuur/BN+xFM8RxG2ToVqdS4+n+a3yJSNcv+WMlz3TkHigGZh//+I4XqVeZeVBz3Ll1BS07h5roQirdraK042ToefYMyJQvjUqIwdrY62tUqx44TpiPgCvj3bjIA/7t7n1KFCwBQwN4WW52hGd1P0aNQZsvV0K0e585FceHCJZKTk1mxYh2dvU1HgTp7e7J48d8ArFrlTysP97Tl7VixYh33798nKuoy585F0dCtHgC7QvZzMyEx0/NpmkbhIoUBKFK0MLFxV8z2tzyOa91aFE17bmtxc6trUt9//70Bb29PkzLe3p4sWbISgNWrA4wd7du377BnTyj37t01KV+hQlnOnLnA9es3Adi2LYQuXTqYNeeKFeuyzGlsF6v98UhrF97enpnahZtbXapWrcT+/Ye5c+cuer2eXcH78PFpn6uc6WVsy8tXrMM7Q1v2zqYte3u3Y3k2bTk7VapUZNeufQAEbd3Fa691fGT5x2ngWpsL5y9yMeoyycnJrF7lTwev1iZlOnZqw7I/VwOwbu1Gmrds8nCdVxuioi5z8sQZk22cnBxo264lixeuyFW+R6lUtzLxUfFcvXyFlOQUdm/YhWtb0+NwxN5w7t+9D8Dpw6co4VgSgLgLscRHxQGQcPUmSdeTKFKiiMWymmaO4+rlK+iTU9i7IQTXto1MykTuPW7MfDZdZn1yCin3UwCws7dD2ZjvmPzovHmrjivXrUxcVBxXLhkyh2wIpqGnaR0f3xvO/bv3jJlLpmUGCN99jDv/u2PxnOmVq1uJaxevcOPyVfTJeg5t2EMtTzeTMmf2RpCcVs9Rh89QzMGQWUPDLp8dtna22NrbobPV8c+1pEzPIZ4Nz3znXClVXil1Uin1u1LquFJqqVKqjVJqt1LqjFKqoVJqvFJqnlJqh1LqvFJqUNrm3wAVlVJHlFLfpy17QSm1Mm2fS5VSFjty2TuU4H7sdePj+3E3sHMomalc8Y6NqbFlOhXnjMDeKfN6S7t66w4ORQsaH5cuWpCr/5gedPq3qo3/0Qt4fr+agYt38HknV+O68MvX6TrTj+4/+TOmc0NjZz23nJwduBwda3wcHROHk5NDtmX0ej1JSbcoWbI4Tk5ZbOtsum1GQ4eP49spY7hwLpTvvvmS0WOmmOXvyCucnByITldnMTFxODmVzraMXq/n1q1/KFky+1G5c+cuUqVKRcqVc0Gn0+Ht7YmLi1Oucjo7ORJ9OS5dznicnB0zlHEgOjrOmDPpVlq7cHY0LgeIiY7H2cmRiMhTNGvWiBIlilGgQH7at29lknNA/z4cDNvCnNk/UKxY0RxndnLOXLfOT9iWnbP6f0lry5qmERjwF/v3BdLvvYefBkVEnDK+YenezYsyuaxzR0cHYmIe1ltsTDyOjqZtw9GpNDHR8cb8t5L+R4mSxSlYsACDh3zAd1NmZdrv5G9HM/7L70i14L2KSziU5Ebcw+PwzbgblMziOPxA655tObzjYKbllepUxtbelisX4y2SM73iDiVMMt+Iu0FxhxLZlm/Zsw1HdxwyPi7h+CLfbvyRn/b9zvrfVpNwNcGiefNiHZdwKMn1WNM6Llk6+8xterbl0PbMmf9LxUqXIDH2hvFxYtwNipbO/vjb2NeDyB1HAIg6dIbTeyP4OnQ2Ew/M5kTwUa6ci7F4ZotLTbXsj5U8853zNJWAGUBtoCrwJuAODAceTGKtCrQDGgLjlFJ2wOfAOU3T6mqa9uAz1nrAp0B14GUg02fsSqkPlFJhSqmwNf9GPX3qrPr9munHJIlbwjjW+EMi2g7h1q5jVPhx8NM/31PSyPzRTcbkG49F0bl+RTaP6MpPvVsyZtUeUtM+8qlV5kVWD/Ji6Yft+SM4gnvJerPkyup9k5ah/rIu82TbZvThB28zbMR4KlR0Y9iICcydPTWHifO2p6/v7Os1MTGJQYNGs3jxz2zdupKLF6NJSUnJZc7My540Z3bbnjx5lu9/+IXAgL/w27CEY+GRxpyz5yyiarWmuLp5Eh9/le++/fIpMlumLbdo2YWGjdrj5d2LAQP64O5uGPl7/4OhDOjfh/37AnmhcCHu30/OcWbTbJmXPWmdfz56EL/+NJ9//71tss6zvQfXrt3g6JHM17hYWnZtttlrLXi5ViXWz15jsrzYS8X5ZPoQfhk+87HHEXPI8hPIbJ7WPS3zhnSZb8Zd57P2nzKkeX+ad/Og6Is5f0OZW898HefgWNbitZZUrF2JtbNXWzrWo2VzjMiKaxd3ytauyLY56wF4sVxpHCo5M7bxAL5s3J8qr9akYsNqlkwrciGvdM4vaJoWrmlaKhABbNUMr6JwoHxaGX9N0+5pmnYduAqUznpXHNA0LTptX0fSbW+kadocTdNcNU1zfa1QptVP7H7cDeydHl6MYe9YkuQrN03K6BP+QUv7CPLa0i0UrPXyUz/f0ypdpCDxSQ9PnFeSbhunrTyw5uA5PGuWBaBO2VLcS0kl8fY9kzIvv1SUAva2nL2aecrI04iJjjMZ8XNxdiQuw1ST9GV0Oh1Fixbh5s0EYmKy2Db20dNU3u7dgzVrDBfVrVy5ATe3/+6C0GdBTEycyWixs7MjcXFXsy2j0+koUqQwN28++v87ICCI5s19aNnyNc6cOc/Zs1G5yhkdE4dLmYcj5c7ODsTFxmcu4+JozFm0SBFu3kwkJvrhcgBnFwdi4wzbLliwjEaNO9C6TXcSbiZy9uwFAK5evU5qaiqapvHHvD+fql0Ynte0bjNOm8quLUdn9f+S1pYfvB6uXbvB2nWBxmynTp2jY6c3adS4A8uXr+P8+agcZ04vNjYe53SfTjg5OxAfb9o2YmPicXZxMOYvUvQFEm4m0sC1DuO/HsmR49vp/1EfhgzrT78PetGocX06dGzNkePb+X3BjzRr3pjf5v6Qq5xZuRl/g5KOD4/DJRxLcjPDcRigVtM6dB3Yg2/7TTJOCwEo8EIBRs3/kr9+WMKZw6fNnu9JMpd0LElCFplrNq1Nl4Hd+aHfZJPMDyRcTSD69GVeaWjZucV5sY5vxF3nRSfTOr55NXPm2u516D7QlynvTcyyjv9LifE3KJbu0/VijiW5lcWnIlWa1sJzYFfm9PvOmLl2u4ZEHT7D/dv3uH/7Hid2HKF8vcqZts1zZM65VaXvBaame5zKw9tBpi+jJ/vbRD5puVz798gZ8lVwxL7MSyg7W0r4uJOwOdSkjF26C3WKebpx92y0peJkq4ZzSS7d+IeYhP+RnKJnU/hFWlQ1vfjIsVhB9p8zdGLOX03ifoqe4oXyEZPwP+MFoLGJ/+Pi9Vs4FTPPxTyhYUeoVKkC5cuXwc7ODl9fHzb4bTYps8FvM7179wCgW7dObN+x27jc19cHe3t7ypcvQ6VKFTgQeviRzxcbd4UWzQ3zZFt5uHMmrXP2/0VY2FGT+u7Rwxs/vy0mZfz8ttCrl+GuBl27dmTHjj2P3W+pUoaTSbFiRfngg97Mn/+XWXP6+vpkmdPYLrp2Ykdau/Dz25KpXYSGHjHJWaaME126GDq1AA4OLxn36+PTnoiIUznOnLEt9/T1wS9DW/bLpi37+W2mZxZtuWDBArzwguG1VrBgAdq2aWHM9uBvUUrxxajBzJmzOMeZ0zt0MJyXK5anbDkX7Ozs6NqtExv9Te92FBiwldffNNxVxKdLe3btNMx579TuTerW9KBuTQ9++2UB06f+xu9zlvD1+KnUrNqMujU96NfnU3YF76P/+8NzlTMrZ4+ewbGCIy+VeQlbO1uaejcjbMsBkzLla1TggykD+Pa9Sdy68XAerq2dLSPmjGLnqu3sC3h8WzeXc0fP4FDBkVJlXkJnZ0sTb3cOZpG535SP+OG9ySaZSziUxC6fPQCFihTiFdeqxJ2LxZLyYh2fOXoGxwpOvFSmNLZ2trh7Nyc0Q+YKNV5mwJSPmfze1yTdsP787EtHz1GqvAMlXEqhs9NR3/tVwreEmZRxqVGe1yf3Y26/7/jfjVvG5Qmx16nUqDo2OhtsbHVUbFSNK1bob4gn87zf5/wfwHpX2elTuTRmLq/8OQ5sbLi+fCt3T1/Gafgb3D56lsQtoZR+txPFPN3Q9HpSEv/HhU8zz8u0NFudDZ97uTJg4TZSUzV86lekUuli/LL1KNWdStKymgtD2zfgq3X7WLrnJCjFhK5NUEpx+OJV5gVHYquzwUbBKC83ihfKb5Zcer2ewZ+OIcD/T3Q2NixYuJzIyNOMHzecsINH8fPbwrz5y1i4YCYnI0NISEjkzV6G638jI0+zcuUGwo9uJ0WvZ9Dg0cZ5rUsW/0yL5k148cUSRJ0PY8JXPzB/wTL69x/BtGlfYWtry727dxkwYKRZ/o4nMWLcN4QePkZi4i1ad+nFR+/1ppv3f3MLtAf0ej2ffvolGzYsRqfTsXDhck6cOM3YsUM5eDAcf/8tLFiwnHnzfiQiIpibNxN5++2Bxu1PndpN4cKFsbe3w9u7HV5evTh58gxTp46nVi3DyN3kyT8aR6Rzm9Pfbyk2OhsWLlhO5InTjBs7nIOHDO1i/vxlLJg/g8jIEBJuJtKrd1q7OGFoF0ePbkOfomfw4DHGdrF82RxKlixOcnIKgwaPJjHRcDKeMnk0derUQNM0Ll68zEcff/5UmQd/Ogb/DG153LjhHEzXlhcsmMmJtLb8Vrq2/PfKDRzL0JZLly7Fyr//AEBnq2PZsrVs3rwDgNd7dqH/gD4ArF0bwIKFy3NT5ej1ekYOn8DKtfPQ2ehYunglJ0+eZdTowRw+HM7GgG0sWfQ3v839gbAjQSQkJNKv75BcPae5pOpT+WPsHEYvGo+NzobtK7YSfeYyPYe+ybljZwkLOkDvL/qSv2ABhv1ieM1fj73Ot/0m0cSrKdUa1qBwscJ4dDfcQvPn4TOJirTsG/dUfSoLxs5l1KJx2Oh07FgRRPSZy3Qf+gYXjp3lYFAob37Rh/wF8zM4LfON2Gv80G8yzpVc6DWmb9o0LoXfnHVcPnXR4nnzYh3P/fI3xi2egI3Ohq3Lg7h8+hJvDH2Ls+FnCN1ygHdG9yV/wfyM+NXwmr8We40p7xnuqjVp5Tc4V3Qhf6H8zN0/n59HzORI8KMHgMyReeXYeXy06AtsdDbsW7GD+DPRdBzSg0vh5zkedBCfUb2wL5ifvr8YXn8JMdeZ+/73HAnYR5VXa/L5ph9A0zix8wjHtx56zDPmAc/pfc7VfzG3KzeUUuUBP03TaqY9XpD2eOWDdcBK4H+apv2QVuY44KVpWpRS6k8Mc9UDAX9guKZpXmnlfgLCNE1bkN3zhzq/9mxXUAY1pz/6Lg7PosK9Zls7Qo7did1l7Qg5VtilpbUj5EhqHjzoPuvH04wK5yv4+ELPmNYl8tbt32zNeAer/0pKdhPcn2Epeex4UcYm7732AGZGLX+mGvS/X/patLEW+nqFVf7eZ37kXNO0KKBmusd9sluXbnn68m9mWL0j3bqBCCGEEEKIvMeK88ItKa/MORdCCCGEEOK598yPnAshhBBCCJGRZsV7kVuSjJwLIYQQQgjxjJCRcyGEEEIIkfc8p3POpXMuhBBCCCHynue0cy7TWoQQQgghhHhGyMi5EEIIIYTIe/LY/e2flIycCyGEEEII8YyQkXMhhBBCCJH3yJxzIYQQQgghhCXJyLkQQgghhMhzNBk5F0IIIYQQQliSjJwLIYQQQoi85zkdOZfO+WM0u3HQ2hFyJLV3qLUj5JiNUtaO8P/CP9E7rB0hx2pV72ntCDnyasFy1o6QIyuvHbJ2hBwLvHbM2hFyJC8e3+7rU6wdIcf0qXprR8iRlwoVs3aEpzLT2gH+n5DOuRBPobBLS2tHyJG82DEXQgghHilV7nMuhBBCCCGEsCAZORdCCCGEEHnPczrnXEbOhRBCCCGEeEbIyLkQQgghhMh7ZORcCCGEEEIIYUkyci6EEEIIIfIcTZORcyGEEEIIIYQFyci5EEIIIYTIe2TOuRBCCCGEEMKSZORcCCGEEELkPTJyLoQQQgghhLAkGTkXQgghhBB5jvacjpxL51wIIYQQQuQ9z2nnXKa1mEnbti04dmw7ERHBDB/+Uab19vb2LF78MxERwQQHr6NcORcASpQoxqZNy7h+/QTTp39lso2dnR0///wN4eE7OHp0G126dDBrZk/PlhwP30lkZAgjhn+cZealS34hMjKEkF0bTDJv3rSCmzdO8eOPE022+WrCSM6dPcDNG6fMmjU3eQFGjviYyMgQjofvpG3bFsblgwb148jhrRw+FMTiRT+RL18+s2a2RLvo3t2b0NBNHDoUxKRJX5g1b06MmTyN5p1ep0uv/lbL8CjuHk0I3LOSTftX8/4n72Ra79q4HquCFnM8di/tvFpZIaFBzRZ1mbx1BlN2zKLjgC6Z1nu+58XELdOZEDiV4UvHUdL5ReO6388tZ3zA94wP+J5P5n5msYxt2jbn4OEgjhzbxpBhmf+/7e3tmb9wJkeObWPbjtWULesMQIMGtQnZ60fIXj927/PHy9vTuE14ZDB7DwQSstePHbvWWSTzoSNbORq+naHZZF64aBZHw7ezfecaY2aPVu7s2r2e/QcC2bV7PS1aNDFu061bJ/btDyQ0bBNfT/zcIpnNWc/58tmzfecadu/zZ3/oRr4Y/alZ8+aV856nZ0uOHw/mRGQII0Zkc+5Y+isnIkPYHZLh3DFyICciQzh+PNjk3FG0aBGWLZtDePhOjh3bQeNGDQAYP34Ehw5uISx0MwH+f+LoWDrX+Vu2dif4gB8hBwP5+NN+WeS349c/fiDkYCAbtvyFSxkn47pqNaqwftNStu1ZR9DuNeTLZw+AT7eOBO1ew5aQ1Sz5ezbFSxTLdU5hPlbrnCulyiuljueg/AKlVPe0339XSlXPokwfpdRP5sz5JGxsbJgxYyI+Pu9Qt25rfH07U7VqZZMyffr0JDExiRo1mjNr1u9MnDgKgLt37zFhwlQ+/3xSpv1+/vknXLt2nVq1WlK3bmt27dpn9szenXtTp44HPXv6UC1D5r59XychMYnq1d2ZOXMuk9M6gnfv3mP8hO/57POvM+3Xzz+Ipu5eZstpjrzVqlbG19eHunVb4eXdi5kzJ2FjY4OTkwMff/wujZt0ol79Nuh0Onx9O5s9sznbRYkSxZgy5Qs6dHiD+vXbULr0i3h4NDVb5pzo0rEtv02b+PiCVmBjY8PYb0fy/huD8XL3pVNXTypWqWBSJi4mnlGDJuC3epOVmaiWAgAAIABJREFUUoKysaHXV/2Y3mcSY9oOoVFnd5wquZiUuRR5ga+8P2Nch2GEBe6lx6jexnX3795nfMcRjO84glnvf2uRjDY2NkydNoFur/XFrUE7uvfw5pWqlUzKvP2OL4mJt6hbuxU//zSPCV8b3ihERp6mhbsP7k286NqlDzNmTUSn0xm369ThTdybeNGymY/ZM0+b/hVdu/TBtb4nPXp0pmqGzO/08SUxMYk6tTz4edYfxs72jRs36dG9H40aduDD94cz949pgOG1N3HyKLw6vYWbazteeulFWrZ81ayZzV3P9+7dx6vjWzRt3ImmTbxo07Y5bm51zZY3L5z3bGxsmDljEt7evahdx4PXe3ahWjXTnO/2fYPEhCSqVXdnxsy5TJ48GoBq1SrT09eHOnVb4eX1FrNmTsbGxtBtmj7tKzZv2k6tWi1o0KAtJ06eAWDq1F+p36Atrm6eBAQEMWb0kFznn/T9aHr16I9H48506daRyq9UNCnzRu9uJCXdwr1BB+b+uojR44cCoNPpmDn7Gz4f9hWtXvWhh1cfkpNT0Ol0fDXlc3p496Wte1dORJ6m7/tv5iqn1aRa+MdK8uTIuaZp/TRNi7R2jgfc3Opy7lwUFy5cIjk5mb//3oB3uhEiAG9vT5YsWQnA6tUBxg7V7dt32LMnlHv37mba7zvv+PLddz8Dhm/BunEjwWKZV6xYl2XmxYv/BmDVan88PNxNMt+9ey/Tfg8cOER8/FWz5TRHXm9vT1asWMf9+/eJirrMuXNRxhOUrc6WAgXyo9PpKFCwAHFxVyyW2RztokKFspw5c4Hr128CsG1biNk/UXlSrnVrUbRIYas89+PUrl+DSxcuE30xhuTkFALWbKF1+xYmZWIux3E68qxV5yy+XLcSVy/Gc+3yVfTJKezfsJu6nm4mZU7ujeD+3fsAnD98huIOJf/TjK6udTh//iJRUZdJTk5m1Uo/Onm1NSnTyasNfy1dBcDaNYHGTuudO3fR6/UA5M+Xj//qy/xcXetw/tzDzCtXbsicuVNbli4xZF6TLvOxo5HExxmOYZGRp8mXLx/29vaUr1CWs+lee9u378anS3vzZrZAPf/7720A7OxssbWzNds3KuaV815Dt3omOZevWIe3d7tMOY3njlX+tDKeO9qxPMO5o6FbPQoXfgF390bMm/8XAMnJySQl3QLgn3/+Z9xvwUIFc13f9RrUIur8ZS5djCY5OZl1qwNo19HDpIxnh1b8/Zfh0yf/dZtxb9EYgBatXuVExGkijxs+yU5ISCI1NRWlFEopChYqAEDhwoW4En8tVzmFeVm7c65TSs1VSkUopTYrpQoopeoqpfYppY4ppdYopYpn3EgptUMp5Zr2e1+l1Gml1E6gaboy3kqp/Uqpw0qpIKVUaaWUjVLqjFKqVFoZG6XUWaXUixmfIyecnByIjo41Po6JicPJqXS2ZfR6Pbdu/UPJkpn+NKOiRYsAMG7ccPbu9Wfp0l956aVcxTTh7ORI9OW4dJnjcXJ2zFDGgejoOGPmpFu3HpnZknKT18nZ0bgcICY6HmcnR2Jj45n+42zOnd3PpYuHuJX0D0FBwWbLbIl2ce7cRapUqUi5ci7odDq8vT1xcXHKtvz/V6UdShEX8/CNVnzcFUo7lrJioqwVK12Cm7HXjY8T4m5QvHSJbMs3821F+I7Dxsd2+ewZu/5bRq+ZTL0MnXpzcUz3ugKIjYnDKcNH9Y5OpU1ee7du/UOJtHbs6lqH/aEb2XsgkE8HjTF2IjVNY+36hewMWUefvq+bNbOTkwPRMRmOF04OGcqUNpYxHC8yv/a6dOnAsaMR3L9/n/PnoqjySkXKlnVOe+21xdmMrz1L1bONjQ0he/04FxXK9m27CQs7apa8eeW85+ScOadzxrbg7MDldDmTkgznDues/kZnB15+uRzXr9/gj9+nE3pgE7N/+56CBQsYy3311WecPxfKG2+8xvgJ3+cqv4NjaWLTteW42Cs4ZGgXDk4vERsTb8x/69Y/FC9RjJcrlgdNY+nKOWzc8TcDBr0LQEpKCqOGfc3WkLUcOrGDyq9U5K/Fq3KV01q0VM2iP9Zi7c55ZeBnTdNqAIlAN2AR8JmmabWBcGBcdhsrpRyBCRg65W2B9FNdQoDGmqbVA5YBIzVNSwWWAG+llWkDHNU07Xq67VBKfaCUClNKhen1/+NxlFKZlmV8t/wkZdKztdXh4uLE3r1hNGnSif37D/LNN2Mem+VJZREn15ktKTd5s9u2WLGieHt5UuWVJpQr34BChQrw5htdzRXZIu0iMTGJQYNGs3jxz2zdupKLF6NJSUnJfdjnzTPUdh8lJ///jbs0o3ztimyc83B+9ohX+/NV58+YM+hH3hjbl1Jlcz+/NXPGzMsytWOyLARAWNhRGrm1p2XzLgwbPsA459WzdQ+aN+1Mt9fe5f0Pe/NqU/O9uTDHa69atcp8NfEzBn1imOKQmHiLTwd/ycLFP7E5aAUXL8agN+Nrz1L1nJqainsTL6pVeZUGDWpTrXoVM+XNG+e9p8+Z/ba2Oh316tVi9uxFuDVsx7//3mbkyIHGMmPHfsvLFd346681fPRR31zmz7zsSduFzlaHW+P6DPxgJF069KZDp9a4N2+Era0tb7/bk3YtulO/WktORJzmkyHv5yqnMC9rd84vaJp2JO33g0BFoJimaTvTli0Emj9i+0bADk3Trmmadh9Ynm6dC7BJKRUOjABqpC2fB7yd9vu7wPyMO9U0bY6maa6aprnqdC889o+IiYkzGb10dnYkLu5qtmV0Oh1FihTm5s3EbPd540YC//57m3XrNgKwerU/devWfGyWJxUdE4dLmYcjz87ODsTFxmcu4+JozFy0SJFHZrak3OSNiX64HMDZxYHYuHhat3InKuoy16/fJCUlhbVrA2ncpIHZMluiXQAEBATRvLkPLVu+xpkz5zl7NspsmZ8XV+Ku4uj8sKPq4Fiaq/HXH7GFdSTE36CE08ORweKOJUm8mvlj/OpNa+E1sBsz+31Dyv2HHcIHZa9dvsrJfRGUrVEh07a5FRsTb/L6cXJ2JC7D1LXY2HiT115W7fj0qXP8++9tqld/BcA4/e36tRv4rd9MA9c6ZsscExOHi3OG40WGKWsxMfHGMobjxcPMTs4O/LlsNh/0G8aFC5eM2wQGbMWjxWu09uhm9teeper5gaSkfwjZtZ82bR91Sn1yeeW8Zzj+m+aMzdgWouMoky5n0aJFuHkzIe2ckuFvjL1CdEwc0dFxHAg1fIq1arU/9erWyvTcy5at4bXXOuYqf1zsFZNPiR2dSnMlQ7swlHEw5i9SpDAJCUnExV5h3+4wEm4mcvfOXbZt2UXNOtWpUasqABejLgOwYe1GGjQyz7UI/7lUzbI/VmLtznn6Sct64GkuF86u9mYBP2maVgv4EMgPoGnaZeCKUqoVhs594FM8p4mwsKNUqlSB8uXLYGdnR48e3vj5bTEp4+e3hV69ugPQtWtHduzY89j9+vsHGe8U4OHRlBMnzuQ2araZfX19sszcu3cPALp17cSOHbvN9vw5lZu8fn5b8PX1McwbLV+GSpUqEBp6hEuXY2nUqB4FCuQHwMPDnZMnz1oss7naRalShjnHxYoV5YMPejM/bd6jeCj8cCTlXi6Lc1kn7Oxs6fhaW7ZtMt+UJXO5cPQspcs78qLLS+jsbGnk3ZQjW0JNypStUYG3J3/IzH7f8M+NW8blBYsUwtbecDfcF4oXpnKDqsSdiTZ7xoMHj/FyxfKUK+eCnZ0d3bp7EeAfZFImwH8rb7zVDYAur3Vg5869AMbpVwBlyjhRucrLXLwUTcGCBXjhhUKGv6NgAVq1dudE5GmzZq5Y6WHm7t29M2cOCOKtXobMr6XLXLRoYVatmsf4sd+xb99Bk20evvaK8P4HvVi4YDnmYol6LvliCYoWNVwXkj9/Plp6NOXMqfNmyZtXznuhYUdMcvb09cHPb3OGnJsfnju6dWK78dyxmZ4Zzh0HQg9z5co1oqNjqVLFcGFmq1bunDhhaL+VKj18g+zt5cmpU+dylf/IoeNUqFiWMmWdsbOzw6drRzYHbjcps3njdnq8YbioupOPJ7uD9wOwc+tuqtWoQv6066oaN3XlzKlzxMddofIrFY1Topq3fJWzZmoXwjyetfucJwEJSqlmmqbtAnoDOx9Rfj8wQylVErgF9AAeTKgrCsSk/Z7xPmq/Y5jesljTNH1uQ+v1ej799Es2bFiMTqdj4cLlnDhxmrFjh3LwYDj+/ltYsGA58+b9SEREMDdvJvL22w8/Ajt1ajeFCxfG3t4Ob+92eHn14uTJM4wZM4V5837k++/Hcf36TT74YFhuo2bK7O+3FBudDQsXLCfyxGnGjR3OwUNH8fPbwvz5y1gwfwaRkSEk3EykV++Ht8o6fWovRYoYMnf2bkenTm9y4uQZpkweTc+eXShYsADnz4Uyf/5ffD1xmlXzRp44zcqVGzh6dBv6FD2DB48hNTWV0NDDrF4dwIH9G0lJSeHIkQh+/31prrNmzGzudjF16nhq1TLM4Jo8+UfOnr1gtsw5MWLcN4QePkZi4i1ad+nFR+/1pluGC62sRa/X8/Xn3/HH8pnY6HSs+nM9Z0+d55PPPuT4kRNs3xRMzbrV+WnBdxQpWgQPT3cGjvwQ7+Y9/9OcqfpUloz9naGLxmCjsyFkxTZiz0TTZUhPosLPcSQoDN9RvclXMD8f/WJ4/d+Iuc6s97/FsZIL70z+IG3qliLg1zXEnjV/51yv1zNi2HjWrFuITmfD4kV/c/LEGUaP+ZRDh8IJDNjKooXLmfP7NI4c20ZCQhJ93xkEQJNXXRkytD/JKSmkpqYy9NOx3LyRQPnyZVi67DcAbHU6/l6xnqAt5nvzpNfrGTZ0HGvXLzJmPnHiDGO+HMKhQ+EE+AexcMFyfv9jOkfDt5OQkESftz8B4MP+7/ByxXJ8NuoTPhtlWObj/TbXrt3gu+/HUqtWNQC+mTLTrK89S9RzjZpV+W3O9+h0OmxsFGtWBbBx4zaz5c0L5z29Xs/gT8fg7/8nOhsbFixcTmTkacaNG87Bg4Zzx7z5y1iwYCYnIkNISEjkrV5p547I0/y9cgPHjm4nRa9n0ODRpKYabuHx6ZAvWbRwFvb2dpy/cIl+/Qx3SJk0aRRVqlRES03l4qUYPv44d7fc1Ov1jBk5iT9XzcFGZ8PypWs4ffIcw0cN5OiRCLYEbmfZ4lXM/O0bQg4GkpiQxEfvDQcgKekWc35ZSMDW5WhobNuyi62bDa+z6d/9wmr/hSSnpBBzOY4hH1nvtry5YsU7qliSst4cYlUe8NM0rWba4+HAC8Ba4DegIHAe6KtpWoJSakFa+ZVKqR3AcE3TwpRSfYFRQBxwBNBpmjZQKeUDTMfQQd8HuGma1jLtueyAG0BDTdNOPipn/vxln72Jqo+Qqj2nLfUZY6Os/aFTzvwTvcPaEZ5Krer/bWc5t14tWM7aEXJk5bVD1o6QY6nP4LUDj2KT1aThZ9x9fd67jkWfmutxtv/US4Xy5n3FYxIinqkGndjTw6IHhGLLt1vl77XayLmmaVFAzXSPf0i3unEW5fuk+71lut/nk/W88XVAdt9sUQfDhaCP7JgLIYQQQohnkzXvqGJJz9q0FotTSn0ODODhHVuEEEIIIYR4Jvy/65xrmvYN8I21cwghhBBCiFx4Tmfy5q2Js0IIIYQQQjzH/t+NnAshhBBCiLzveZ1zLiPnQgghhBBCPCNk5FwIIYQQQuQ9MudcCCGEEEIIYUkyci6EEEIIIfKc5/V7F2XkXAghhBBCiGeEjJwLIYQQQoi85zkdOZfOuRBCCCGEyHNkWosQQgghhBDComTk/DFSUvXWjvDcU9YO8BRSn9fP0p4x4ZHLrR0hx14s39baEZ5Yqpb3vsDjbsp9a0fIkbx4fCtW4AVrR8ixW/duWzuCsIbn9FQsnXMh/h+oVb2ntSPkWF7smAshhBC5JZ1zIYQQQgiR58iccyGEEEIIIYRFyci5EEIIIYTIc2TkXAghhBBCCGGklGqvlDqllDqrlPo8mzK+SqlIpVSEUurPx+1TRs6FEEIIIUSeY+2Rc6WUDvgZaAtEA6FKqfWapkWmK1MZGAU01TQtQSn10uP2KyPnQgghhBBC5FxD4Kymaec1TbsPLAN8MpR5H/hZ07QEAE3Trj5up9I5F0IIIYQQeY+mLPqjlPpAKRWW7ueDDAmcgcvpHkenLUuvClBFKbVbKbVPKdX+cX+WTGsRQgghhBAiA03T5gBzHlEkq+8Zy/jtbrZAZaAl4ALsUkrV1DQtMbudSudcCCGEEELkOdaec45hpLxMuscuQGwWZfZpmpYMXFBKncLQWQ/NbqcyrUUIIYQQQoicCwUqK6UqKKXsgdeB9RnKrAU8AJRSL2KY5nL+UTuVkXMhhBBCCJHnaKlZzSr5D59f01KUUgOBTYAOmKdpWoRS6isgTNO09WnrPJVSkYAeGKFp2o1H7Vc650IIIYQQQjwFTdMCgIAMy8am+10Dhqb9PBHpnAshhBBCiDznGZhzbhEy5zyH5s6ZSmz0UY4c3pptmRbNmxAWupmjR7axLWhljp/js5EDORkZQsTxYDzbtjAuP3t6H4cPBREWupl9ewMesYestfNsScTxYE5GhjByxMeZ1tvb2/Pn0l85GRnCnpANlCvn8shM+fLlY+9uPw6GbeHokW2MGzvMWP6jAX04GRlCyv0YSpYsnuOsD3h6tuT48WBORIYwIpvMS5f+yonIEHZnyDxy5EBORIZw/HgwbdPV45ls6vHLL4cSdSGMsNDNhIVupn37Vk+fOXwnkZEhjBieTeYlvxAZGULIrgyZR3xMZGQIx8N3mmQeOPA9Dh8K4sjhrXzyyXsPM48ZyoXzYYQe2ETogU1PnflJuHs0IXDPSjbtX837n7yTab1r43qsClrM8di9tPOyXI7cGDN5Gs07vU6XXv2tmqN1m+aEHdrC4aPbGDL0w0zr7e3tmb9wJoePbmPr9lWULWu4M1f9BrXZtWcDu/ZsIGSvH17ensZtihYtzKIlPxF6aDMHDm7CrWE9s+Vt07Y5h45s5Wj4doYOy1x39vb2LFw0i6Ph29m+c40xbwPXOuzZ58+eff7s3ReAd+eHeX/57VsuRIVyIHRjrrKZ+7j2qH1md1wbNrS/8bhx5PBW7t25RPHixZ4ov7mPcVWqVDRmCQvdzI3rJxn0ST/AfMe4B1q1bsbesI0cOLyZQUPezyK7HXPnT+fA4c1s3LqCMmntokxZZy7FH2X7rrVs37WW76dPAKBAgfz8uWI2e0ID2bXPjy/HD8u0z9zybNuS8GM7iIzYxfDhH2WR2Z4li38hMmIXu4LXG+u7RIlibNq0nBvXT/Lj9K+z3PeqlfM4dDDI7JlbtnYn+IAfIQcD+fjTfllktuPXP34g5GAgG7b8hUsZJ+O6ajWqsH7TUrbtWUfQ7jXky2dPoRcKsjl4lfEn/GwIEyZn+cWWwkryxMi5UmoHMFzTtLBHlOkDuGqaNtCSWRYtWsEvv8xn/vwZWa4vWrQIs2ZNppPXW1y+HEupUiVztP9q1Srj6+tD7bqtcHIqzabAZVSr0YzUVMPbwzZte3DjRkKOc9vY2DBzxiTad3yD6Og49u0NYIPfZk6cOGMs827fN0hISKJqdXd8fTszZfJo3nxrQLaZ7t27RxtPX/799za2trYE71jDxo3b2X/gEHv2huIfEMTWLTl/c5Ixc4d0mf2yyJyYkES1tMyTJ4/mrbTMPX19qJOWeWPgMqo/QT3OmDmX6dNn5yrzjBkT6djxTaKj49i7x9+Q+eTDzH37vk5CYhLVq7vj26Mzkyd9wVu9PqJaVUM9103LHBj4FzVqNKda1cq89+4bvNrUi/v3k/HzW0Jg4DbOnr0AwMxZucv8pH/X2G9H8m6PgVyJvcLfmxeybVMw505fMJaJi4ln1KAJvPtRL4tmyY0uHdvyZrfOfPH1D1bLYGNjw9Rp4+nS+R1iYuLZHryGgICtnDp51ljm7Xd6kJiYRL06rejW3YsJX39G33cGcSLyNC2bdUGv11O6dCl27/MnMGArer2eb74bS9CWYN7uNRA7OzsKFsxvtrzTpn9FZ6/exMTEE7xrHQH+QZxMl/edPr4kJiZRp5YH3bt78fXEz3nn7U+IjDhFs6adDXkdSrFvXwAB/oa8SxevYvZvi5g7d2quspn7uAZku8/sjmtTp/3G1Gm/AeDVqS2DB71PQkK2d0nLlN+cx7jTp8/h6uZp3P/FqIOsXRdo3F9uj3Hps38zdSw9uvQlNuYKm7evZGPANk6fOmcs89bbPUhMvEXDep506daRsROG837fIQBEXbiER7Mumfb786x57N61Hzs7O1avX0DrNs3ZGhSc67wPMs+YMZGOnQzH5z27/fDz28LJ9MfnPq+TmJhI9RrN6NGjM5MmfkGv3h9x9+49Jkz4gRrVX6FGjVcy7dvHpz3/+/dfs+TMmHnS96N547X3iYu9QsC25WwO3M6ZdPX8Ru9uJCXdwr1BBzp37cDo8UMZ8N5wdDodM2d/w+D+o4g8forixYuSnJzCvXv38Wzezbh94PYVBPhtMXv2/4KmWXfOuaXIyHkO7QrZz81HHHTfeP011q4N5PJlw510rl17OOf/zTe7sne3H2Ghm/nl52+xsclc/Z2927FixTru379PVNRlzp2LoqFb7kfAGrrV49y5KC5cuERycjIrVqyjs3e7DM/tyeLFfwOwapU/rTzcH5vp339vA2BnZ4utnR2GqVVw5EgEFy9GmzXz8hXr8M6Q2TubzN7e7VhugXp8HDe3upnq2Tvd6GamzKv98TBm9sxUz25udalatRL79x/mzp276PV6dgXvw8fnsd9hYFa169fg0oXLRF+MITk5hYA1W2jdvoVJmZjLcZyOPIuWmvEWr88O17q1KFqksFUzNHCtw/nzF4mKukxycjKrV/rRqVMbkzIdO7Xhz6WrAVi7JpAWLZsAGNsAQP78+Yyvt8KFX6BpUzcWLVwBQHJyMklJ/5glr6trHc6fe5h35coNdPJqa1KmU6e2LF2yCoA1awJp2fLVzHnz5UNL1zR27z5Aws3Hd2AfxRLHtUft80mOaz17+rBs+dqnym/uY1yrVu6cP3+RS5dinihPTtRvUJuo8xe5GBVNcnIya1f706FTa5MyHTq2YvmfawDYsHYTzVo0eeQ+79y5y+5d+wFDGz52NBJH59Jmy5zp+Pz3+qyPz0sMb75Wr/bHw6MpALdv32HPnlDu3ruXab+FChVk8OD3mTJlptmyPlCvQS2izl/m0kVDPa9bHUC7jh4mZTw7tOLvv9YB4L9uM+4tGgPQotWrnIg4TeTxUwAkJCQZB6geqPByWV4sVYL9ew6aPbt4ehbpnCulRiqlBqX9Pl0ptS3t99ZKqSVKKU+l1F6l1CGl1N9KqRfS1jdQSu1USh1USm1SSjlm2K+NUmqhUmpi2uO+SqnTSqmdQNN05byVUvuVUoeVUkFKqdJp255RSpVKt6+zabe1MZvKlV+mWLGibN3yN/v3BdKrV3cAqlathG+PzjRr0QVXN0/0ej1vvtk10/ZOTg5cjn54i8zomDicnB0A0DSNwIC/2L8vkH7vvZWjXE7OWezXySHbMnq9nqSkW5QsWfyRmWxsbAgL3UxczDG2bg3mQOjhHOV6XObodM8bExOH8xNmdnbKvO2T1ONHA/py6OAW5s6ZSrFiRXOc2dnJkejLcemeNx4nZ8cMZRyIjo57mPlWWj07OxqXA8REx+Ps5EhE5CmaNWtEiRLFKFAgP+3bt8LF5eHHlgP69+Fg2BbmzP7hqTI/idIOpYiLuWJ8HB93hdKOpSzyXM87J6fSxESbthFHJ9MOiKOTg7GMXq/nVtI/lEibRtHAtQ77QgPZsz+AIYO/RK/XU758Ga5fv8kvv33Hrt3rmfXTZAoWLGCmvA5Ex2Ro0xlfh06ljWUMbfof47QPV7e6hIZtYn/oRgYPHm3srJslmwWOa0+yz+wUKJCfdp4tWb3myaYdWuoY90BPXx+WZ3ijkNtj3AOOTqWJiYk3Po6NuYKjo2k7dnAsTUy6dnHr1j+UKGFoF2XLubBt1xrW+S+mcZMGmfZfpGhhPDt4sGvn3qfOmFHG//Ms6ztdvT7I/LipmePHjeDHH+dy584ds2V9wMGxNLHpXn9xsVdwyFjPTi8Rm/Z/8SBz8RLFeLliedA0lq6cw8YdfzNg0LuZ9u/TrRPrV+duapk1aamW/bEWS42cBwPN0n53BV5QStkB7kA4MAZoo2lafSAMGJq2fhbQXdO0BsA8YFK6fdoCS4HTmqaNSeu4T8DQKW8LVE9XNgRorGlaPWAZMFLTtFRgCfCgN9YGOKpp2nVz/uG2tjoa1K+Nt8/bdOz0JqNHfUrlyi/TysOd+vVqsW9vAGGhm2nVyp2XK5TNtL1SmT+ieTA61rxlFxo2ao+Xdy8GDOhDM/dGT5zrUft9dJlHb5uamoqrmyflKrji5lovy4/7npalMrfIUI/uafU4e/YiXqn6Kg1cPYmLv8r3343NtI/HZ8687Mkya9lue/LkWb7/4RcCA/7Cb8MSjoVHkpKSYsg8ZxFVqzXF1c2T+PirfPftlznO/ESe4P9CPJns2qxpmczbPajvg2FHaezWAY8WrzF0WH/y5bPH1taWOnVr8MfvS2nWtDP/3r7DkCzmhpsv75O1aYCw0CO4ubajRTMfhg3/iHz57M2SK3fZHvU6fPq27uXlyZ69YU80pSX7bLk/xgHY2dnh5eXJylV+xmXmOMblPrvGlfir1KvhQatmr/Hl6G/47fepvFC4kLGMTqdjzh/T+P23xVyMyt0nsDnPnHm7R/3/165dnYoVy7F+vWU6uE90TsnqSyo1DZ2tDrfG9Rn4wUi6dOhNh06tcW9u2m/w6dqBtatyfg3bs0JLVRaNPxV4AAAgAElEQVT9sRZLdc4PAg2UUoWBe8BeDJ30ZsAdDB3p3UqpI8A7QDngFaAmsCVt+RgM37T0wGzguKZpDzrsjYAdmqZd0zTtPrA8XVkXYJNSKhwYAdRIWz4PeDvt93eB+VmFV0p9oJQKU0qFpabmbA5ZTEwcmzZv5/btO9y4kcCukH3Url0dpRSLl/yNq5snrm6e1KjZnK++noaPT3vjxTkN6tcmJiaOMulGRV2cHYmLNYxYxsUZ/r127Qbr1gXi5lb3yXNFZ7HfuCvZltHpdBQtWoSbNxMememBpKRb7AzeQzvPlk+c6Ukypx8hdnZ2JPYJM0fHZN42q3pcm64er169TmpqKpqm8ccfS3HNQf0+EB0Th0uZhyPlzs4OxMXGZy7j4vgwc5Ei3LyZmPb3ptvWxYHYOMO2CxYso1HjDrRu052Em4nG+eYmmef9maM2kRNX4q6afLzs4Fiaq/FmfV/7/0ZMTDzOLqZtJD5Du45NV0an01GkaOFMU0BOnzrHv7fvUL36K8TExBETE8/BsKMArFsbSJ06NTCHmJg4XJwztOmMr8OYeGMZQ5suzM0MeU+dOsftf29T3Yxv4C1xXHuSfWanp2/nJ57S8iCbJY5xAO3be3D4cDhXrz58nZrjGPdAbEw8zulG6p2cSxMff9WkTFxsPM7p2kWRIoVJSEjk/v1k4xuYY0ciiLpwiYqVKhi3mzbja86fi2L2rwufOl9WMv6fZ1nfMfHGen2QOWNbTq9xowbUq1ebU6f2sG3raipXrsDmzSvMljku9orJp6+OTqW5kqmerxg/NXlYz0nExV5h3+4wEm4mcvfOXbZt2UXNOg/HMavXfAVbWx3hRyPNlleYh0U652lfURoF9AX2ALswfDtSReACsEXTtLppP9U1TXsPUEBEuuW1NE1LPxlsD+ChlEp/lVN2b2dnAT9pmlYL+BDIn5brMnBFKdUKQ+c+MKuNNU2bo2maq6ZprjY2hbIqkq31Gzbh3rQROp2OAgXy07BhPU6ePMO27SF0fc3LeIFo8eLFKFvWmXXrNho77AcPHWOD32Z8fX2wt7enfPkyVKpUgQOhhylYsAAvvGDIUrBgAdq2aUFExKknzhUadoRKlSpQvnwZ7Ozs8PX1YYPfZpMyG/w207t3DwC6devE9h27jcuzyvTiiyUoWrQIAPnz56d1q2acSneRSm5lzNzT1we/DJn9ssns57eZnjmsRweHl4z77eLTIUf1+0BY2NFM9eyX4UIbP78tDzN37cQOY+Ytmeo5NPQIgLHdlCnjRJcuHVi+fF2mzD4+7Z8q85MIPxxJuZfL8n/s3XdcU9f/x/HXIYLVuuqoAq662tpWraN17y0oKmrrbqt+q3Wvure2tnXX1i2K1r0VEBEVcVRlqSBuHCBOwA4Hhvv7IyESwAqaiPj7PB8PHia55968c7259+STc28cizpga5uF5q0b4bvbMidp/X8TGHCSkiWLU6xYYWxtbWnj6oSHh/nVnzw89tKxk2HYm0vrZvgZv9ovVqwwOp0OMGwLpUu/x5Wr17l16w6RkTcoVdrQwalTt7rZCaYvIyDgJCVLPc3r6uqMxy7zK1J4ePjQqbPhBLPWrZtxINW8jpQuU4KrL3kuSlLW2K+lZZmpyZUrJ7VrVWX79t0vnN8S+7hEHTq4pBjSYol9XKKgwFO8V7I4RY3bhUubFnh5+Jq18fLwpUPH1gA4uzTB3+8oAPnyvWM656pY8cKUKFmcKxHXABg5ZiC5cudg9IhpL5ztWQz75+JP/2/btUx9/2wcjtomyf75WRYtdue9EpV5//3q1G/QhvPnL9O4cXuLZQ4OPM17JYtSpKgjtra2tGrTHG/PfWZtvL320e7LVgC0aNWYQ36GcfsH9h7iw4/K8Fa2t9DpdFStUdnsRNJWbZtn6qo5GL5FsuZfRrHm1Vr8gKEYKtSngJkYKupHgflKqVKapl1QSmXHUOk+CxRQSlXTNO2IcZhLGU3TQo3LWwrUBjYopVoDfwJzlFL5gPtAOyDE2DY3kHgGTPJrvi3BMLzFXdO0dA9+XOU+nzq1q5E/f14iLp1g4qRfsLW1BQxv0vDwC+z23kdQoA8JCQksW7bGtAMcN+EnPD3WYGOjiI9/Qv/+o1OcqBMWdo6NG3dwKmQfT/R6+g8YTUJCAgULFmDjhqWAYejM2rVb2e29P8259Xo9AwaOwWPXH+hsbHBbsY6wsHNMGD+UEwEh7Ny5h2XL17LCbS7hYf7ExMTSsXOf/8xkb1+QZUtno9PZYGNjw8aNO9jlYTho9/3ua4YO6UOhQgUICvDB08uX/307LF3rOjHzrmSZx48fSkCSzG5uczljzNwpSeYNG3dw8j/Wo864Hr2N6/HHH8ZQvnxZNE0j4sp1+vT5Pl15EzMPHDiWXTtXY6OzYYXbOsLOnGP8uKEEBBoyL1++FrflcwgL8yfmXiyduxgznzGs55AQX/RP9AwYMMZ08s66tYvIl+8dw3YzYDSxsXEA/DBtNOXLf4SmaVy5co0+31nnclh6vZ7JI35i6bq52Oh0bPpjOxfOXqLf9//jdPAZ9u324+MKZfnV7Sdy5c5FvcY16Tv8fzjX7mCVPC9q2PgfOR50ktjY+zRw6Uyfb7rQNtkJeNam1+sZOmQim7e6odPZsMp9I+FnzjNqzECCAk/h6bEX9xXrWbRkBkEhvsTExPJ19wEAVK1WmUFD/kd8/BO0hASGDBrPPeNVh4YPmciSpbOwtbMl4vI1vus93GJ5hwwez9btK9HpbHBfuYEzZ84zZuwgAgNP4bHLhxVu61iydBYhp/YRExNH9679AKhWvQpDhnxL/JMnJCQkMGjgWNNVkpa7zaFW7arky/cOZ88fZuqU2aYTWtOTzdL7NSDVZcJ/79dcWjVjj48f//6b9nHH1tjHgWHse8MGtVPswyyxj0uafeTQSazfvAQbnY41qzZxNvwC34/qT3DQaXZ7+rLafSO/LfqZY0HexMTE0etrw5VaqtWowvej+vPkiZ6EBD1DB40nNiYOe4eCDB7Wm3NnL+LrZziRdOniVaxa+eJX/UqeeeDAsezcsQqdTofbinWcOXOOceOGEBhwkp279rDcbS3Ll80mLPQg9+7F0qXr08tbnj17mFw5c2JnZ4uzcxNaOHUyu9KLNej1esYMn8ofmxZho7Nh3eotnAu/yNCRfQkJDmWP5z7Wum9i7oIf8Q/wJDYmjj7fDAUM32gv+m0FHnvXoaHhu+cge72fFlWcXZrQpX1vq+YXL0ZZa9yoUqoB4AXk0TTtH6XUOWCBpmkzjZXr6UBWY/MxmqZtV0pVAOZi6FxnAWZrmrY46aUUlVITgTIYxo53A0YCN4BgQKdpWl+lVCtgFoYO+lGgiqZpdY25bIG7wGeapoU/73VksXOUgbVWlhkvhJTa2MXXWYnc9s9v9Jo5Fbbu+Y1eQ/mLN3p+o9eEPiEDz3h6QQ+fPM7oCOmSufYUBnmy5cjoCOl2/9G/GR0hXfJny5XREV5IZEzoa7VJX6nY0Kp9tGKBPhnyeq1WOdc0bS9gm+R+mSS3fYEqqcwTjKE6nvzxukluj08yaTmpjBvXNG0bsO0Z0cpjOBH0uR1zIYQQQgghXqVM8SNElqKUGgH05ukVW4QQQgghRCaUkVdUsab/Vz9CpGnaj5qmFdM0zT+jswghhBBCCJHc/6vKuRBCCCGEeDO8qT+38f+qci6EEEIIIcTrTCrnQgghhBAi05Ex50IIIYQQQgirksq5EEIIIYTIdDRNKudCCCGEEEIIK5LKuRBCCCGEyHS0zPcjx2kilXMhhBBCCCFeE1I5F0IIIYQQmU6CjDkXQgghhBBCWJNUzoUQQgghRKbzpl6tRTrnz5HZ/tttbDLflyH6hEx4Rkcm+83g6tmLZXSE/zfuROzJ6AjpUqRUi4yOkC7ZbO0yOsIb79/4RxkdId1UJjta69/UMxmFRUjnXAjxWspfvFFGR0i3zNYxF0KIzOxN/YVQ6ZwLIYQQQohMJ5N9iZ1mmW8MhBBCCCGEEG8oqZwLIYQQQohM500d1iKVcyGEEEIIIV4TUjkXQgghhBCZjvwIkRBCCCGEEMKqpHIuhBBCCCEynTf1R4ikci6EEEIIIcRrQirnQgghhBAi05HrnAshhBBCCCGsSirnQgghhBAi05GrtQghhBBCCCGsSirnQgghhBAi05GrtYgUGjeuy+nTfpwJ82fYsO9STLezs2P16t85E+bPIf8dFCtW2DRt+PC+nAnz5/RpPxo1qmM2n42NDceP7WbrlhWmxxYt/IWAE3sIDNjD2rWLePvt7C+fv1FdTp3cT1joQYYO7ZNq/lXuvxEWepCDfttN+fPmzcPu3eu4eyec2bMmm9pny/YWW7e4cTJkH0GBPkyZPOKlMybVpHFdQk/7ER7mz/BnrO8/Vv9OeJg/h5Ot7++H9yU8zJ/Q0340Nq7vwoUd8PHewKmT+wkJ9qVf328sktMa28X5c0cJCvThxHFvjh7xMD1erlxZDvptJyjQhy1b3MiZM4dFXkOij+tUYNreOfywfx7Ne7ukfK3fODFlzywmes5g6Orx5HPMb5q25OI6Jnj8zASPn+m3+HuL5kqqQcPanAjcQ1CIL4MG/y/FdDs7O5avmEtQiC97922iaFFHACpWKsfBwzs4eHgH/kd24uTc2DRP7tw5WbnqV44HenMsYDdVPvvUavn/y5hpM6nd4gtcOn+bIc+fqF6Dmvgf9+BIoBd9B/ZIMd3OzpaFy2ZyJNALD5+1FCnqYJr24Udl2Om9hgNHdrDv0DayZrUDYMSYAQSc9uXi9RNWyVy/QS2OnPDiWJA3/Qf1TDXz4uWzOBbkjdfe9RQxbhdFijpyNTqEfQe3su/gVn6eNdE0z7pNS9jnv42DR3fy86yJ2NhY9hBq6czZsr3FH+sXcvi4JweP7mTshCEWzduoUR2Cgvdy8tR+hgzpnUpeO1as/JWTp/az/8BWihY17Ovq16+J/6EdHDvmhf+hHdSpUy3FvOs3LOb48d0WzZuY+eTJfYSG+j3zuOfuPp/QUD/8/LYlO+6t5c6dM8yaNclsHldXZ44f301goA9Tp46yeObM+P4TL+eVdc6VUhFKqfypPH7Y2s9hDTY2NsydMxVn586UK1+PLzq48OGHpc3afP3Vl8TGxPFh2ZrMmbuYadNGA/Dhh6Xp0L4V5SvUx8mpE/PmTjPbyffv14Mz4efNljVk6AQqVW5ExUqNuHY1kj59vnrp/HPmTKFlq66Ur1CfDu1b8cEH5vm/6v4FsbGxlP2oFnPnLWHqFMNO5+HDR0yc+AsjRkxJsdxZsxdSrnw9Pvu8GdWqV6FJ47ovlTNp3rlzpuLk3JlPytejwzPWd0xMHB+UrcnsuYv5Icn6bt++FeUq1KdFkvX95MkThg2fyCfl6lKjpjO9e3dPscwXzWmN7aJho3ZUrtKYqtWamx5buOBnRo2exqcVG7Jtq2eqB8gXpWxs6DypB7O6T2VMo0F83rImDqUKm7W5GnaZSc7fM77ZEE54HqHdyC6maY8fPmZC82FMaD6MeT2nWyxXUjY2NsyYOQHXNl/zWeUmtG3nzPsflDJr07VbO2Jj4/i0fH1+m7+ciZMNHxTOhJ2jbi0XalV3pq3LV8yeOwWdTgfAjz+Nw2ePH1UqNqZGVSfOnb1glfzP49K8EQtmpnyfvUo2Njb88MtYOrr2ovbnzrR2bUGZ90uatenYxZXY2DiqVWzKwt9WMmbCUAB0Oh3zF/3E8METqFPNmTZO3YiPfwKAt9d+mjXoYLXMP84YxxeuPajxWQtat3VKkblT13bExt7ns08bs+A3N8ZNHGqaFnH5KvVquVCvlgvDBo03Pf5N9wHUq9mKWlWdyJ//HVq2bvraZ54/bxnVqzSjfq3WfPZ5RRo0rG2xvDNnTaK1S3cqVWxEu3Yt+SDZe69b9/bExsZR7pO6/DpvKZOnGAo2d+/G4Or6DZ991pRePYewZOkss/latmrCP3//a5GcyTPPmTOFVq26UaFCA9q3b5niuNe9ewdiY+P46KPazJu3hClTRgKJx70ZjBgx1ax93rx5+OGHUTRr9iUVKzakYMH81KtXw6KZM9v771XSNOv+ZZRX0jlXSumeNU3TtOqvIoOlfVblUy5ejODy5avEx8ezbv02nJ2bmLVxdm6Mu/sGADZt2kX9ejWNjzdh3fptPH78mIiIa1y8GMFnVQyVOUdHe5o1a8CyZWvMlvXXX3+bbmfL9hbaS241VapUMMu/fsN2nJNUDk35V20EYPPmXaYdzr//PuDw4eM8fPTIrP2DBw85cOAIAPHx8QQHncKxsP1L5UyUfH2vX7+NlsnWd8tnrO+Wzk1Yn8r6jo6+RVDwaQD+/vsfwsPP4+hQyKI5LbVdPEuZMiU5ePAoAD57D9K6dfP/bJ8eJSqU4taVaG5fu4U+/gl/7jhEhcZVzNqEHwnl8cPHAFwKOs87hfJZ7PnTolLl8ly6dIWIiGvEx8ezeeNOWrRoaNameYuG/LF6MwBbt3hSp66hSvfgwUP0ej0Ab72V1fSeypkzBzVqVGHlivWAYVuOi/vrVb0kM5UrfELuXDkz5LkTfVqpHJcvXeXqlevEx8ezdZMHTZrXN2vTpHl91q/ZBsDObbupWacqAHXr1yDs9FnCTp8FICYmloSEBAACT4Rw6+Ztq2SuWKkcEZeucCXCmHnzLpq1aGDWplnz+qz7YwsAO7buplYq1dvk/v7rHwCyZMmCra2tRY/e1sj84MFDDh38EzBsxydDwrB3LGiRvJUrV+DSxafvvY0bd+DkZH4McWrRmNWrNgGwZYsHdesaDvchIaFE37gFQFjYObJmzYqdnaGi+/bb2enXrwfTp8+zSM6kkh/3NmzYkepxb5XpuOeR4rj36NFDs/bvvVeU8+cvc+fOPQB8ff1xcWlmscyZ8f0nXt5zO+dKqeFKqf7G27OUUr7G2w2UUquUUl8qpU4ppU4rpaYnme9vpdQkpdSfQLUkj2dTSnkppXomtjP+W1cptV8ptVEpFa6UWq2UUsZpzY2P+Sul5iqldhofz6eU8lZKBSmlFgIqyfNsVUoFKKVClVK9jI99o5SalaRNT6XUzBdZcQ6Ohbh+Pcp0PzLyRoqOnYNjIa4Z2+j1euLi7pMv3zs4OqSc18HRMO+MGRMZOXKK6Q2U1JLFM7l+LZj33y/F/PnLXiT202wOT7M9M3+SnHq9nvv3/yJfvnfStPzcuXPRokVD9u079FI5TVkczfNej7yBQxrXd/LXej3J+k5UrFhhKpT/mD+PBb10TmtsF5qm4emxhj+PetLjm06mNqGhZ00HF9e2ThQp7ICl5CmYl3tRd0z3Y27c5Z2CeZ/Zvlb7+pza/3T92Wa1Y9z26YzeMo1Pk3XqLcXBoSCR12+Y7kdGRmPvYN75sHcoZGqj1+u5H/cXeY3bcaXK5Tl63JPDf3owaMBY9Ho9xYsX4c6de/y24CcOHtrOvF+nkT17Nqvkzwzs7d8lKjLadP9G1E3s7ZOtY/uCREU+Xcd/3f+LvHnzUKJUcTRgzabFeB/YxHf9LTN07LmZHQoSmSRzVGTKzIXsCxKZJPP9+3+RN69huyharDC+B7ewbZc7VatVMptv/eYlnLl4mL///oftWy037MKamQFy5c5J42b1OGgsoLwsB4eCXI80318lf+8lbfOsY4iLSzNOhoTy+LHhQ/64cUOYO3cJ//5r3gm2TOZU9rEpMqfvuHfx4hXKlClJsWKF0el0ODs3prAF98OZ8f33KiVoyqp/GSUtlXM/oJbxdmUgh1LKFqgJnAemA/WBCkAVpVTiwNS3gdOapn2uaZq/8bEcwA7gD03TFqfyXJ8CA4GyQAmghlLqLWAh0EzTtJpAgSTtxwP+mqZ9CmwHiiaZ9rWmaZWMmfsrpfIBa4GWxvwAXwHLk4dQSvVSSp1QSp1ISPgn1ZVi/NxgJnk1O/U2z563efOG3L51h8CgU6k+Z4+egylarCLh4edp365lqm3SKm35U86Xloq9TqfDfeWvzJ+/nMuXr75wRvMsll/fid5+Ozvr1y1m8NDxZt9QvE4569R14bPPm+Lk3JnevbtTs+bnAPTsNZje33bnz6Oe5Mj5No8fx79U/ufnTP3/v6pLLYqXK4nXom2mx4ZV/5ZJLb9nUf/ZfDnuKwoUtUzF7vkZk7dJOV/i6wg4EULVKs2oV6c1g4d8S9asdmTJkoXyFT5i6ZLV1KrRkn/+fcCgIRk75jsjpbqOSds2nUWn4/OqFfmu5zBaNe1EM6eG1Kxd1WpZ/ztPWjJr3Iy+xacf1aN+rdaMHf0jC5bMIEfOt01t2rfpwcdlapI1qx216ljutVgzs06nY9HSmSxZ4M6ViOuvLG9qb76kbT78sDSTp4ygXz/DkMly5cpSomQxdmy3/FhzQ5wXX8fPEhsbR//+o3F3n8/evRu5cuU6T548efmw/5XnNX//iZeXls55AFBJKZUTeAQcwdDhrQXEAvs1TbutadoTYDWQOKBND2xKtqxtwHJN01Y+47mOaZp2XdO0BCAYKA58AFzSNO2ysU3S8R61gVUAmqbtAmKSTOuvlAoBjgJFgNKapv0D+AJOSqkPAFtN01L0hDVNW6RpWmVN0yrb2LydfDIAkddvmH06dnS0J+rGzRRtEiuZOp2O3Llzce9eDNcjU857I+om1atXxsmpMefPHWX1qt+oV68GK9zmmi0zISGB9Ru207p1i1RzpVVk5A2zKmuq+SOjTTl1Oh25cuXk3r3Y5y77t9+mc+HCZeb9uvSlMppluW6et7CjPTfSuL6Tv9bCxvUNhq+nN6xbzJo1W9i61dMiOS29XQCm13r79l22bvOkSpUKAJw9e5HmLTryedVmrFu3jUuXIl76NSSKib5LXoenp3C8Y5+P2FsxKdqVrfEJTn3bMrfHjzx5/PSglNj29rVbhB8NpehH71ksW6LIyGizoVOOjoWITra+o5K00el05Mqdk5hk2/G5sxf5598HlC37PpGRN4iMjCbgRAgA27Z6Ur78RxbPnllERd00+6bJ3qGgaUjC0zbRODg+Xcc5c+UkJiaWqKibHDl0nHv3Ynnw4CF79/hRrnxZ62eOjMYxSWYHx4JER5tnvhEVjWOSzLmMmR8/jicmxrB9nAwOJeLyVUqWMt92Hz16jJeHL82amw87eV0zz5wzmUsXI1j4+wosJTIymsKO5vurFNtFkjbJjyEOjoVYs3YhPXsMNhVxPvu8Ip9++glhZ/zx2buBUqXfw9NrrQUzp7KPTZY5aZu0Hvc8PHyoXbsVdeu25vz5S1y4EGGxzJnx/fcqaZqy6l9GeW7nXNO0eCACQ5X5MHAQqAeUBP6rLPpQ0zR9sscOAc1Uah/zDJIOYtZjuNTj89ZOio+0Sqm6QEOgmqZp5YEg4C3j5CVAd55RNU+r4yeCKVXqPYoXL4KtrS0d2rdi505vszY7d3rTpUs7ANq2bcG+/YdMj3do3wo7OzuKFy9CqVLvcex4EGPG/Mh7JSpTukxVOnXuw759h+jWvT8AJUsWNy3XqUUjzr7kCWonToRQqlRxU/727Vqyc+eeZPn30KWzKwBt2rRg//7nD1GZMGEYuXPlZMjQCS+VL7nk67t9+1bsSLa+dzxjfe/Y6U37VNY3wOJFMzgTfoHZcxZZJacltovs2bORI4fhQ2L27Nlo1LAOoaGGMYQFChjGeCulGDVyAIsWuVvkdQBcDrlAweL25C/8LjrbLHzuXIPgPcfN2hT96D26Tvsfc3v8yF9375sez57rbbLYGa7UmuOdnJSu9AE3zlumYpdUYMBJSpYsTrFihbG1taWNqxMeHnvN2nh47KVjpzYAuLRuhp/xa/3Er6EBihRxoHTp97hy9Tq3bt0hMvIGpUobOjd16lbnbHjGnBD6OggOPEWJksUoWswRW1tbXNo2x9tzn1kbb899tP+yFQBOrZpwyM9wHsT+vf58+NH7ZMv2Fjqdjmo1qnDu7EWrZw4KPMV7JYtT1LhduLRpgZeHr1kbLw9fOnRsDYCzSxP8jZnz5XvHdCJ2seKFKVGyOFcirvH229kpWNDwxa1Op6Nh4zqcP3fptc4MMHLMQHLlzsHoEdMslhUgICCEkqWevvdcXZ3Ztcv8GLLLYw+dOrcFoHXr5hw4YLj+Q+7cudi8aTnjx/3E0aMBpvZLFq+iVMnPKfthTRo2aMeF85dp1vQLi2U2HPee7p/btXNO9bjX2XTca87+/c+/ZkXifjhPntz06tWF5cvXPGeOtMuM7z/x8tJ6nXM/YCjwNXAKmImhon4UmG28QkoM8CXwX2dxjAPGAr8Bab2sRDhQQilVXNO0CCDp6cV+QCdgilKqGZA4MCw3EKNp2r/GCrnpexxN0/5UShUBKgLl0pghBb1ez4CBY9i16w90Nja4rVhHWNg5xo8fSkBACDt37mHZ8rW4uc3lTJg/MTGxdOpsuGxTWNg5NmzcwcmQfTzR6+k/YHSqY8wTKaVYtnQ2uXLlAKU4dTKM7/qOfNHopvwDB45l545V6HQ63Fas48yZc4wbN4TAgJPs3LWH5W5rWb5sNmGhB7l3L5YuXZ9eFvDs2cPkypkTOztbnJ2b0MKpE3/99RcjR/QnPPw8fx41VKF/X+DG8uUvX/lIXN8eydb3hPFDOZFkfa9wm0u4cX13TLK+N27cwalk67tG9Sp06ezKyVNhnDhu6ECPHfsjnl6+/xUlTTktuV0ULFiAjRsM30LosuhYu3Yr3t77Afiigwvf9u4OwNatHritWPfC2ZNL0CewatwSBq8cg43OBv/1vkSdv47LoA5EnLpIsM8J2o/sQtbsb9HnN8Ml2u5G3mFez+nYlypMt2m90DQNpRQev28h6oLlO+d6vZ6hQyayeasbOp0Nq9w3En7mPKPGDCQo8BSeHntxX7GeRUtmEBTiS0xMLF93HwBA1WqVGTTkf8THP0FLSGDIoM70ljIAACAASURBVPHcu2uo9g8fMpElS2dha2dLxOVrfNd7uMWzp8Ww8T9yPOgksbH3aeDSmT7fdKFtshOMrU2v1zNq2BTWbFqCTmfDmlWbORt+geGj+hEcdBpvz3384b6RXxdO50igF7Excfzva8P2EBd3n4Xz3fDy3YCmaezd44eP9wEAxk4cSmvXFmTLno3AUMMyfvlxvsUyjxw6ifWbl2Cj07Fm1SbOhl/g+1H9CQ46zW5PX1a7b+S3RT9zLMibmJg4en09CIBqNarw/aj+PHmiJyFBz9BB44mNiaNAgXy4r/0dOzs7dDob/P2O4rbMclVda2S2dyjI4GG9OXf2Ir5+hhNJly5exaqVGy2Sd8jgcWzbvhKdTsfKles5c+Y8Y8YOIjDwFB67fFjhtp4lS2dy8tR+YmJi6da1HwD/+7YrJUoWY8TI/owYaShAtXTuwu3bd1861/MyDxw4lh073NHpdKwwHfcGExBwil279uDmto5ly2YTGurHvXuxdO3a1zT/2bOHyJnkuOfk1Jnw8PPMmDGBTz4xVKSnTZvNhQuXnxXhhTJntvffq/Sm/kKoSssYYqVUA8ALyKNp2j9KqXPAAk3TZiqlOgIjMVS4PTRNG26c529N03IkWUYEhuEwd4FlwG1N04YntjNWu4dqmuZkbP8rcELTNDellDPwM3AHOAYU1DStk3Ec+RogP3AAaANUAv4CtgKOwFkM49QnaJq237jsEUAFTdOe+5Hc1s4xAy+mk36Wvu7uq6D/jw8mr6vMtjvo4vD8K1G8bjbdebmTczPCnYg9z2/0milS6uWGyL1qei3z7S8ym3/jHz2/0Wsmsx1H8ryV+pDZ11107JnX6vD3p0Mbq/bRPo/anCGvN02Vc03T9gK2Se6XSXL7D+CPVObJkex+8SR3v0rezthx3p/k8b5J2u/TNO0D43CY+cAJY5u7QNLrIA1Kcvu/rmVUE5j1H9OFEEIIIcRrLFNVT9Mhs5RZeyqlgoFQDENWFr7IQpRSeYxV/wfGDxxCCCGEECITelMvpZjWMecZStO0WVig0q1pWixQ5rkNhRBCCCGEyACZonMuhBBCCCFEUhl5uUNryizDWoQQQgghhHjjSeVcCCGEEEJkOpnrGj1pJ5VzIYQQQgghXhNSORdCCCGEEJmOlul+dSRtpHIuhBBCCCHEa0Iq50IIIYQQItNJeEN/hUgq50IIIYQQQrwmpHIuhBBCCCEynQQZcy6EEEIIIYSwJqmcp0EWXeZZTQlaAioTfpLM89bbGR0hXeIe/kPOrNkzOka6bLwdmNER0kUphT7hTb2K7evj2oVdGR0hXRxKNsM+W96MjpFmNx7cwyFbvoyOkS4X4qO4UPG9jI6RLu+duEi2LHYZHSNduuapkNER0mVlbHBGR0jhTb1aS+bpdWaQzNQxB6Rj/opIx9z6MmPHvEipFhkdIV0yW8ccIOqiJ5U+7pTRMdIss3XMgUzXMQekY/4KZMbMmVXm6nkKIYQQQgiB/EKoEEIIIYQQwsqkci6EEEIIITKdN3XMuVTOhRBCCCGEeE1I5VwIIYQQQmQ6MuZcCCGEEEIIYVVSORdCCCGEEJnOm1o5l865EEIIIYTIdOSEUCGEEEIIIYRVSeVcCCGEEEJkOglvZuFcKudCCCGEEEK8LqRyLoQQQgghMp0EGXMuhBBCCCGEsCbpnFtIo0Z1CAnx5fTpAwwd2jvFdDs7O9zdf+X06QP4+W2laNHCANSvX5NDh3Zy/PhuDh3aSZ061U3zTJgwjPPnj3D7dpjVMp88uY/QUD+GDu3zjMzzCQ31w89vG8WKGTLnzZuH3bvXcufOGWbNmmQ2j6urM8eP7yYw0IepU0dZNG/9hrU4GuDFseA99B/UK5W8tixZPptjwXvY7buBIkUdzaY7FrYnIiqI7/p9bXqsV++uHDy6E/8/d/G/Pt0smhegQcNa/Bm4mxPBPgwYnFpmO5a6zeZEsA97fDemmvnqjWD69v/G7HEbGxv2+29jzYZFFs/csFFtAoJ8CD7py6Ah36aaefmKuQSf9MV3/2aKGjNXqlQO/yM78T+yk0NHd+Hk3Ng0z6kwP44c88T/yE72H9xm8byBwXsJObWPwc/Iu2LlPEJO7WPfgS1P81Yuz+Gjuzh8dBdHjnrg3PJp3t8WTOdyxHGOHfeyaNZE9RrUxP+4B0cCveg7sEcqmW1ZuGwmRwK98PBZS5GiDqZpH35Uhp3eazhwZAf7Dm0ja1Y7AEaMGUDAaV8uXj9hlcxpNWbaTGq3+AKXzin/LzJKjXpV2e6/lp1HNvB13y4ppleqWoF13m4EXj9II6d6psff/6g07jsXsfnAajb6utOkVYNXlrl6vc/Z5r+GHUfWp5q5YtUKrPVeTsB1Pxomy7xy5yI2H1jFBt+Vryxz1s+rUOCPFRRYu4q3O3+ZYnq2Zk14d8cW8i9fTP7li8nm1Nw0LWfvXuRfuYz8K5fxVv16Kea1lgYNa3Ms0JuAkL0MHPy/FNPt7OxYumIOASF72bMv5f65cGF7rkWHpNg/vypl6pRn2N4ZDN8/i7q9W6aYXrVTQwZ5TWegxw/03jCed0s5prKUzE2z8l9GeS0650qp7kophyT3I5RS+a3wPB5KqTzGv5S90RdkY2PD7NmTadWqG59+2pB27VrywQelzdp0796BmJg4Pv64DvPmLWXq1BEA3L0bg6vr11Sp0oSePQezbNks0zweHj7UqtXKUjFTZJ4zZwqtWnWjQoUGtG+feubY2Dg++qg28+YtYcqUkQA8fPiIiRNnMGLEVLP2efPm4YcfRtGs2ZdUrNiQggXzU69eDYvlnT5jPB3a9qRGlea0cXWizPslzdp06tqO2Ng4PqvQiAXz3Rg/cZjZ9Ck/jGLvHj/T/Q8+LE2Xbu1pXM+VOtVb0rhJPUqULGaRvImZf5oxgfZtelCtSjPaujrx/vulzNp07upKbOx9KldoyO/zlzNhknnmaT+ONsuc6Ns+3Th39qLFsibNPGPmRNq2/ooqlZrg2s6Z9z8wz9y1W3tiY+9ToVx95v+6jImTvwcgLOwcdWq2omY1J9q4dGfOvCnodDrTfC2adaRmNSfqWnCbtrGxYeasSbRx6U7lio2N7z3zvN26tyc2No7yn9Rj/rylTJ5ieO+FhZ6lVo2WVK/aAheXbsydO9WUd7X7JlxculssZ/LMP/wylo6uvaj9uTOtXVuk2JY7dnElNjaOahWbsvC3lYyZMBQAnU7H/EU/MXzwBOpUc6aNUzfi458A4O21n2YNOlglc3q4NG/EgplTMjqGiY2NDaN+GELvjoNxqf0lzVo3okSZ4mZtbkRGM2bAZDy37DF7/OGDh4zuN4k2dTrR+8tBDJ80kJy5cryizEPp03EIrWt3pGnrhikyR0dGM3bAlFQzj+k3iTZ1OtPny8EMmzTA+pltbMg1eAD3ho7gdufuZGvYgCzFU+5LH/ru485XPbnzVU8e7PQAIGu1qtiWKc2dr3pwt1cf3u7YAZU9u3XzYljHP8+cQLs231C1clPatnNKsa/r0q0dcbFxVCrfwLB/njzcbPrU6aPxSWX//CooG0XrSV+xtPt0ZjQaSoWW1VN0voO2HWJW0++Z3XwkBxbuxHlsyg954vX0WnTOge6Aw/MapYVS6pnj6DVNa65pWiyQB7BY57xKlQpcvBhBRMQ14uPj2bBhB05OjczaODk1YvXqTQBs3uxB3bqGTmtISCg3btwCDJ2brFmzYmdnqIQdOxZEdPQtS8VMNfPly1dNmZ2TVDoBnJ0bs2rVRlPmxI72v/8+4PDh4zx69NCs/XvvFeX8+cvcuXMPAF9ff1xcmlkkb8XK5bh86QpXjOt4y6ZdNGvR0KxNsxYNWLtmCwDbt3pRq261JNMaciXiGmfDL5geK/N+SQKOh/DgwUP0ej2HDx2jRbL/t5dRKVnmzZt20czJvIrVvEVD1v6xGYBtW72onSRzc6eGRERcI/zMebN5HBwK0ahJXdxXrLdY1kSVK5fn0qUrpm1508adKdZJC6eGrDFuy1u3eFK3ruHbnsT1CPBW1qxor6DsULlyeS5dfJp348YdKfO2aMTqVYa8W9KY99ChY8Tci7VK5k8rlePypatcvXKd+Ph4tm7yoEnz+mZtmjSvz/o1hm8Ydm7bTc06VQGoW78GYafPEnb6LAAxMbEkJBh+hiPwRAi3bt62Sub0qFzhE3LnypnRMUw+/rQsVy9fJ/JqFE/in+C11Yd6TWqbtYm6Fs35MxdN6zLRlUvXuHr5OgC3b97h3p0Y3smX55VkvpYsc90mtV7bzLYffoD+ehT6qBvw5AkPfHzJWjNthZksxYvxODgE9AloDx/y5MJFslb9zKp5wfDN2aWk++eNu2ie4pjSkDWrDceUbVu8qJNs/3zlcsr986tSpEIp7lyJ5t61W+jj9YTsOMJHjSubtXn09wPTbbvsWdFexU75FUuw8l9GeaHOuVJquFKqv/H2LKWUr/F2A6XUKqVUY6XUEaVUoFJqg1Iqh3H6OKXUcaXUaaXUImXgClQGViulgpVS2YxP0884/yml1AfG+d9WSi0zLiNIKdXK+Hh34/PsALyVUvZKKT/j8k4rpWoZ2yVW5H8EShqn//ziq8/AwaEQ16/fMN2PjLyBo2OhVNpEAaDX67l//y/y5XvHrE3r1s0JCQnl8ePHLxspjZmjzDI7OBRMd+akLl68QpkyJSlWrDA6nQ5n58YULmyRz1zY2xck6nq06X5UVDT2yfLa2xck0vj/kJg3b953yJ49G/0H9eTnH381a38m7DzValTmnbx5yJbtLRo2roNDYXuL5DXkKURk5NPtIioyGnv7ZJkdChJpfF16vZ77cX+TN58h84BBvfjph3kpljtt+mgmjP0pxUHZIpmTbctRkTdwSCXz9eTr2bhdVK5cnj+Pe3HkmCcD+48xdX41TWPr9hUc8N9G96++sFheB4dCXI9M+t6LxsEh+XuvoKmNXq8nLsl2XLlKBY6f2M2fx70YMGC0Ka812du/S1Tk0235RtTNlNuFfUGikmT+6/5f5M2bhxKliqMBazYtxvvAJr7LoK/TM5OC9gW4GfW0yHHzxi3etS+Q7uV8/GlZbG1tuRYRacl4qXrXvgDRUTdN92/duE3BF8r84SvJrCuQH/2tp+s44fZtdAVSfvn9Vp3a5HdbQp7JE7B51/B64i9cJOvnn0PWrKjcubCrWAHdu+l/rell2Pcm2z+nOAYmO6aY7Z//x/RU9s+vSu6C7xAXddd0P+7GXXIVTHl8rtalEd8fmE3zER3ZPmHFq4woXsKLXq3FDxgCzMXQsc6qlLIFagKngDFAQ03T/lFKfQ8MBiYBv2qaNglAKeUOOGmatlEp1RcYqmnaCeM0gDuaplU0Dj8ZCvQARgO+mqZ9rZTKAxxTSvkYM1UDymmadk8pNQTYrWnaVKWUDkj+HdkI4GNN0yq84Os3o1I5WTj5J1SVSqOkbT78sDRTpozAyamzJSI91/PypLVNUrGxcfTvPxp39/kkJCRw9GgA771X9OXDpjFLqm3Q+H5UfxbMd+Off/41m3b+3EXmzlrMpq3L+eeffwk9FY7+yROL5DXkSflYWtfxiNH9+f3X5SkyN25aj9u37xISHEqNmpavLqUpc2pnxxvbnDgRwudVmlLm/ZIsXPQLe7z38+jRYxo3aEd09C3yF8jHth0rOXfuIocPHbdA3pfbjk8cD6ZK5Sa8/35JFi6egfduQ15retZ2+tw2GmTR6fi8akWa1mvHgwcP2bBtOSHBofj7HbVa3kwvnfux1OR/Nx/T5o1jTP/Jr6T6mJb34fPkfzcfU+eNY0z/KdbPnHpgs7sPDx3hgY8vxMeTvZUzeUaP4N6AITw+foJHH75P/gW/khAbS/zpMDS99WuWaTq+PXP/PIDf56fcP79Sqa7zlA8dcd/DEfc9VGhZnfr9WrN+yO/Wz/YKJaS2Ht4AL9o5DwAqKaVyAo+AQAyd9FrAdqAscMi48dsBR4zz1VNKDcfQWc4LhAI7nvEcm5M8Vxvj7cZAS6XUUOP9t4DE3t8eTdPuGW8fB5YZPzBs1TQtOD0vTinVC+gFkCVLXrJk+e/xepGR0RROUnF1dLQnKknVw9DmBoULOxAZGY1OpyNXrpzcM35t7uhYiHXrFtGjx2AuX76anqgvLDFP0syJw2vSkvlZPDx88PAwfF765puO6C20k42Kisah8NOKqINDIaKT5Y2KisaxsD03om6a8sbci6Vi5fI4t2rC+EnDyJ07FwlaAg8fPWbpolWsdt/IanfD0J3R4wYTFRWNpURFRePo+HS7cHAslGKYUlRkNI6FCxEVZVzHuXMQcy+WSpXL07JVUyZMHm7InJDAw4ePsHcoSLPmDWjUuA5Z38pKzpw5WLD4F77tOTT5079Y5mTbsoOjPTeSZ44ytDFlTmW7OHf2Iv/88y9ly75PUNAp0+u+c/suO7d7G07GtEDnPDLyBoUdk773CnHjRvL3XjSFHe2JMm7HuVPJe/bsRf7951/KfvQ+QYGnXjrXf4mKuolDkm/W7B0KprotOzg+3ZZz5spJTEwsUVE3OXLouCn/3j1+lCtfVjrn/+Fm1C0KOrxrul/Q/l1uR99J8/xv58jO/FUzmDd9EScDQ60RMYWbUbcplKSK+659AW6lM/Ovq37h1+mLOPUKMutv3Ub37tN1bFOgAPo7d83aaPfvm27/u2MXOXs/PUH+75Wr+XvlagDyjB+D/tp1KydO3Pcm2z8nfx8a2yTfP1euUp5WLk2ZmGT//OjRYxYvdLd67kRx0ffI7ZDPdD+3fT7u34p5ZvuQHUdoPUW+acssXmhYi6Zp8UAE8BVwGDgI1ANKApcxdJQrGP/Kapr2jVLqLeA3wFXTtE+AxRg618/yyPivnqcfIhTQNsmyi2qadsY47Z8k+fyA2kAk4K6U6prO17dI07TKmqZVfl7HHAzVwlKl3qNYsSLY2trSrp0zu3aZn6Sza5cPnTq1BaBNm+YcOHAYgNy5c7F583LGjfuJI0de3VUWEjMXL/40886d5pl37txD586upsz79x9+7nILFDDsLPLkyU2vXl1YvnyNRfIGBZyiRIniFC1WGFtbW1q3bYGXx16zNl4evnzxZWsAWro05eABw2dC56YdqfhJfSp+Up+Fv69g9i8LWLpoFQD58+cFDFdFcWrZmM0bd1okL0BgwClKlHyauU3bFnjtMs/s6bGXLzoaPnu2cmnKwQOGTlaLJh2p8HE9KnxcjwW/uTFrxgKWLFrF5Akz+PiDWlT4uB49ug/koN9Ri3XMAQICTlKiZHGKGTO3dXXCY5ePWRuPXXv50rgtu7RuxgHjek4czgRQpIgDpcuU4MrV62TPno0cOd4GIHv2bNRvUJMzYecslrdkqad5XV2dU+b18KFTZ0Pe1s/M60jpMiW4esX6nYLgwFOUKFmMosUcsbW1xaVtc7w995m18fbcR/svDSfOOrVqwiFj53v/Xn8+/Oh9smV7C51OR7UaVaxyYvCbJDT4DMVKFMGxqD1ZbLPQ1KUh+70PpmneLLZZmL18Ojs2eLJnh6+Vkz4VGnyGoiUKm2U+4O2fpnmz2GZh1vIfjZn3PX8GC4gPD0dXxBGdfSHIkoVsDevz6JD58cImX17T7aw1q/PkirEQZWODypXLkL1kCbKULMGj4y//wf15AgNOUrJksaf7Z9cWeKY4puzly06GY0qr1k3xM+6fmzf+kvIf1aX8R3X5/Tc3Zv7y+yvtmANcD7lI/uKFeKdwAXS2Oso7VyNsT4BZm/zFnxYBPqj/KXcjLFd8el28qVdreZkfIfLDMNzkawxDWWZiqHIfBeYrpUppmnZBKZUdKAwkfiS9YxyD7gpsND72F5CWM4h2YxiL3k/TNE0p9ammaUHJGymligGRmqYtVkq9DVQEViZpktbnSxO9Xs+gQePYsWMlOp2OFSvWc+bMecaOHUxg4El27fLBzW0dy5bN4vTpA8TExNKlS18Avv22GyVLFmfEiH6MGNEPAGfnLty+fZepU0fSoUMrsmfPxoULR1m+fC1Tp862WOaBA8eyY4e7MfM6zpw5x7hxgwkIOMWuXXuMmWcTGurHvXuxdO3a1zT/2bOHyJkzJ3Z2tjg7N8HJqTPh4eeZMWMCn3xSFoBp02Zz4cJli+UdMWwSG7YsxUan4w/3jZwNv8CI0f0JDjyNl6cvq1du4LdFP3MseA+xMXH0/GrQc5e7fNWv5M2bh/j4JwwfMpG42PvPnSc9mYcPncjGrcvQ2ehY7b6R8PALjBw9gKCgU3h5+LJq5QYWLP6FE8E+xMTE0iMNma1Jr9czbMgEtmxbgU5ng/vKDYSfOc/oMQMJDDyFp8deVq5Yx6IlMwk+6UtMTBxfdesPQLXqlRk0+FvinzwhISGBwQPHce9uDMWLF2H12gWAYVjGhvXbLXaFA71ez5DB49m6faUp75kz5xkzdhCBgafw2OXDCrd1LFk6i5BT+4iJiaN7137GvFUYMuRp3kEDx3L3rqHytNxtDrVqVyVfvnc4e/4wU6fMZqWFTsDV6/WMGjaFNZuWoNPZsGbVZs6GX2D4qH4EB53G23Mff7hv5NeF0zkS6EVsTBz/+3oIAHFx91k43w0v3w1omsbePX74eB8AYOzEobR2bUG27NkIDDUs45cf51skc3oMG/8jx4NOEht7nwYunenzTRfaOjd55TkS6fV6po2awe9rZqPT2bB1zU4unr1Mn+E9CQs+w35vfz6q8CGzl/1Irjw5qdOoJr2H9aBNnU40admAilUrkPudXLTsYLj039gBUzgbat2TAPV6PT+Mmsnva2Zho9MlydyD0OBwDhgzz1r2gylzn2Hf0KZO51Qzjxsw1bqZ9QncnzmXvDN/AhsbHuzy5MnlCHJ88xXx4Wd5dOgwb7u2MZwkqteTcP8+sVN/NMybRUe++XMA0P79l9hJU+EVDGvR6/UMHzKRTVuXo9PpWO1u2NeNHDOA4MDTeHrsxX3FehYsmUFAyF5iYmL5pvtAq+dKqwR9AtvGudFj5UhsdDYcX7+fm+ev03iQK9dPXSbMJ4Dq3RpTqsYnJDx5woO4f1j3hg1peZOpFx2LppRqAHgBeYxjy88BCzRNm6mUqg9MB7Iam4/RNG27UmoK8AWGqvs14IqmaROUUm2BacADDGPHzwCVNU27o5SqDPyiaVpd48mis4HqGKroEZqmOSmluhvb9zVm6wYMA+KBv4GumqZdVkpFJFnuH0A5wFPTNPPr1yWRLVuxTHV6c2Y8GzuH3X99gfJ6Sshk6/lJgvVPdLS0zLaOIfNty9cu7MroCC+k0sedMjpCmqV6jsZrzqtU1uc3es2UDb6W0RHSpWf+Khkd4YX8FLHmtdqg19l3suqBosON1Rnyel+4cq5p2l7ANsn9Mklu+wIptjxN08ZgOFk0+eObgE1JHiqeZNoJoK7x9gMgxS8FaJrmBrglub8CSHFasqZpSZfbMbXXJYQQQgghREZ5mWEtQgghhBBCZIiE16qObzmvy48QCSGEEEII8f+eVM6FEEIIIUSmk5AJz+lIC6mcCyGEEEII8QKUUk2VUmeVUheUUiP+o52rUkozXujkP0nnXAghhBBCZDoZfZ1z46/QzweaYfgBzi+VUmVTaZcT6A/8mZbXJZ1zIYQQQgiR6SQo6/6lwWfABU3TLmma9hhYC7RKpd1k4CfgYVoWKp1zIYQQQgghklFK9VJKnUjy1ytZE0cMv9uT6LrxsaTL+BQoomlamn+CXE4IFUIIIYQQmY61f0tW07RFwKL/aJJafd00IkYpZQPMArqn53mlci6EEEIIIUT6XQeKJLlfGIhKcj8n8DGw3/gr9VWB7c87KVQq50IIIYQQItNJy0mbVnYcKK2Ueg+IBL4ATL9Ar2laHJA/8b5Saj8wVNO0E/+1UKmcCyGEEEIIkU6apj0B+gK7gTPAek3TQpVSk5RSLV90uVI5F0IIIYQQmU4ar6hiVZqmeQAeyR4b94y2ddOyTKmcCyGEEEII8ZqQyvlzVMhbIqMjpItTFoeMjpBus2KOZ3SEdGuQN8VvDLzWPG+fzOgI6fbwyeOMjpBu2WztMjrC/wsBp1dndIT00cdndIJ0+az81xkdId0+zl0soyOky/efRD2/kXgua1+tJaNI51wIIf6fqvRxp4yOkG6ZrmMuhBDpJJ1zIYQQQgiR6byplXMZcy6EEEIIIcRrQirnQgghhBAi09Feg6u1WINUzoUQQgghhHhNSOVcCCGEEEJkOjLmXAghhBBCCGFVUjkXQgghhBCZjlTOhRBCCCGEEFYllXMhhBBCCJHpaBkdwEqkci6EEEIIIcRrQirnQgghhBAi00l4Q69zLp1zIYQQQgiR6cgJoUIIIYQQQgirks65FVStW4W1fivY4L+KLt99mWJ6hc/L4ea1kINXfKjXorbZNP+rPqzwXswK78X8tHzKq4pMiTrl+Nb3Z3ofmEG13s4pplfs1ICeu3+kh8c0um4cR/7SjgA4lC9BD49phj/PabzfpLLVMtZvWIujAV4cC95D/0G9Uky3s7NlyfLZHAvew27fDRQp6mg23bGwPRFRQXzX72vTY99+1x3/P3dx8OhOFi2bSdasdlbLX6HOp8zx/Y15Bxbg0rttiulOPVoyy+dXfvGaw7g/JpHfsQAAxcu+x9Qt05m5Zx6/eM2hulNNq2Vs2Kg2gcF7CTm1j8FDvk0x3c7OjhUr5xFyah/7DmyhqHEd16tfk4OHtvPnMU8OHtpOnTrVTPO0bduCo396cvzEbiZPGfHC2Zo0rkvoaT/Cw/wZPuy7VLP9sfp3wsP8Oey/g2LFCpumfT+8L+Fh/oSe9qNxozrPXWaf3t0JD/PnyeNI8uV7x/T4kMHfcuK4NyeOexMctJdHD67yzjt50v1a6jeoxZETXhwL8qb/oJ6pvBZbFi+fxbEgb7z2rjdty0WKOnI1OoR95GamjwAAIABJREFUB7ey7+BWfp410TTPuk1L2Oe/jYNHd/LzrInY2Fhv916jXlW2+69l55ENfN23S4rplapWYJ23G4HXD9LIqZ7p8fc/Ko37zkVsPrCajb7uNGnVwGoZ02PMtJnUbvEFLp1TbvMZxf/PAJw69abZl71YsmpjiulR0bf4ZuAYWnfvR/f+o4i+dcc0bebvbrh064tLt7547j34SvJWr/c5W/zXsO3IOr7q2znF9IpVy/OH9zKOXz9AQ6e6psftCxdk9e6lrPVxY+OBVbh2dXkleQE+q1uF1X5urPFfSafvvkgxvfznn7DUawH7rnhTN9mx+l2Hd5nxx3Tc9y/Dfd8yChUu+Eoy21b8jDy/u5Nn4Wrecu2Yahu7mvXIPX8Fuee7kWPoWABsChQk96xF5J6zhNzz3cjatOUryWttCVb+yyiv/bAWpVQeoKOmab9ldJa0sLGxYcjUAQz4chi3btxmmccCDnofJuL8FVOb6MibTB40nU7fdkgx/6OHj+nWOOXB2pqUjaLp5O780ekH7kff4+vtkznvE8id85GmNqe3HSZw9V4ASjesSMMxnVjb7Sdunb3OUucxaPoEcrybhx6e0zjnE4imt+xmbWNjw/QZ43Ft9RVRkdHs2b8JL4+9nDt70dSmU9d2xMbG8VmFRrRu24LxE4fR46uBpulTfhjF3j1+pvuF7AvS839dqPFZcx4+fMQSt9m0btuCtX9ssWj2xPzfTP4fkzuN5170XX7Y/gsnfI5x/fw1U5vLoZf53mkwjx8+pnHnpnQZ2Z1ZfX/m0YNHzBs0m+iIG7zzbl6m75pBsF8Q/97/x+IZZ86aREunLkRGRuN3cBseu3wID79gatOte3tiY+Mo/0k9XF2dmDxlBN269uPu3Xu0c+1B9I1blC1bhq3bV1CmVDXy5s3DlGkjqVWjJXfu3GPhol+oW7c6+/cfTne2uXOm0rT5l1y/foOjRzzYsdObM2fOm9p8/dWXxMTE8UHZmrRv35Ifpo2mY6fefPhhadq3b0W5CvVxcCjIbs+1fPhRLYBnLvPwkePs8vBh7x7zTtGMmQuYMXMBAE4tGjGgf09iYmLT/Vp+nDGOdi5fERV5E+99G/Hy8E1lW77PZ582xqVtc8ZNHErPrwYBEHH5KvVqpezAfNN9AH//ZdgmlrvPpWXrpmzd5JGubGnNP+qHIfRqP4CbN26xxmsZ+70PculchKnNjchoxgyYTPc+nczmffjgIaP7TeLq5esUKJiftd7LObzvT/66/7fFc6aHS/NGdGzbklGTf8nQHIn0ej1TZi1k8cxJFCqQjw69hlCv5meULF7U1OaX35bRskk9WjVrwJ8BIcxetJIfxwzmwJHjhJ2/yMalc3gcH0/3/qOoVbUSOd7ObrW8NjY2jPhhCL3bD+TmjVus9lrCAW//ZNvETcYPmErXPuYFq9s379Ld+VviH8eTLXs2Nh5w58Buf27fvIM12djYMHhqfwZ9OZzbN26z2OM3DnkfMTtW34y8xbRBP/HFt+1SzD9mzvesnPsHJw4GkC37WyQkvILrhtjY8Pa3A7k/dggJd2+Te+ZC4v88hP7a08w29o5kc+3E/eHfof3zNyq3oXiQEHOXuGHfwZN4eCsbeX5dzuNjh9Du3bV+bpFumaFyngfok9Eh0qrspx9wPSKKqKs3eBL/BJ9tvtRuUsOsTfT1m1w8c4mEhNdjtJRDhZLci7hJ7LXbJMTrCdtxlDKNKpm1efz3A9Nt2+xZTbefPHxs6ojrstqiWWn/VLFyOS5fusKViGvEx8ezZdOu/2PvvuOiOB4+jn/2jkNRAaU3FVtiF3vvLRYs0egv0dij0Ri7MXajMdHYotEYe9do7AVRFBvGhoIaFUEEpRwdJLEBd/v8cXhwghGUE8kz77x4xbud3fsyDLuzs7MLHTq1MSjToVNrft+h61gf3O9J0xYNMy1rw4PQMO5m6mgCmJiYUNisMEqlkiJFzIiKijFK/vJuFYgKjSImLJq01DTOHzpHnbb1DMrcunCTlGcpAAT63cXK0RoAdUgkUaFqABJjEngU9wgLK4s8z1inTg3uBz8gNL2Od+8+RKfObQ3KdOrUlm1b9wCwb99RWrRoBMCN67eJUuvq7vbtQAoVKoSpqSmuZUpxLyiEuLgEAE6dOk/Xbh/lOlu9ujUJDg4lJOQhqamp7Np1gC7u7Q3KdHFvx5YtfwCwZ88RWrVskv5+e3btOkBKSgqhoWEEB4dSr27Nf92mv/8tHjwI/9dMvXt35fed+3P9vdSqXZ3Q+w94EBpOamoq+/ceoUMnwxHkDh1bsTP9JPHQ/mM0zXQl4lVedMxNTExQqVQY65exas3KPAwJJ+JhJGmpaXjuP0HL9oajipFhUQTdCc6yj3twP4yHIbp6jY2OIyEukRLWub/ykNfquFXD0sI8v2Po3bwTRClnR0o6OaBSqejQuinePpcMygSHhlG/dg0A6tWqzqn05cGhYdStURUTEyVFzArzYTlXfC5dM2reqjUrEZapTRzbf5IW7ZsalFHr24Rhu0xLTSM1JRUA00IqJOnd3OFXqWZFIkIjUKcfq08eOEWT9o0Myrw4VssvZXatUBqliRLfc1cBePrkGc+fPTd6ZpMKldCoI9BGqyEtjednvVHVN7ySWri9O8889iE/1p3wyo/SBw/S0nQdc0BSqcCIV9beJdnIX/mlIPx05gHlJEnylyRpgSRJEyVJuiJJ0g1Jkr4DkCTJVZKkAEmS1kqS9JckSdskSWojSdJ5SZKCJEmql15uliRJWyRJ8k5/P8+HqG0dbIiJzOjgxahjsXWwyfH6poVMWe/xG2sOrcjSqTcWcwcr/lZnnD0nqxMwdyiRpVztfm0ZcXYxrSd/yrGZm/TvO7mVY6jXfIYem4fn1PV5PmoO4OhoT2R4lP51ZGQUjk72WcpEhOs6sRqNhuTkv7GyKkGRImaMGvsFC+YtNygfpY5mxS/r8L91mltB50lO/pvT3ufzPDuAlYM18eqMkaAEdTzWDtavLN+6d1v8Tl/N8n75GhUwMTUh+kFUNmu9HScnB8Ij1PrXERFRODk5vFTGXl9Go9HwKPlvg2kfAN26deDG9VukpKRwPziUDz4sR6lSziiVStzd2+Ls4pT7bM4OhIVH6l+HR6izZstURqPR8OhRMtbWJXByymZdZ4ccbfNVzMwK075dC/buy/3ItKOTPRERmdpyRDSOjoZt2cHRnoiIrG0ZoFRpF7zP7ePAkS00aGh4Er1r71ruBP/JP/885uD+Y7nOlhP2jrZEZ9rHRatjsHO0zfV2qtasjEqlIiw04vWF/5+JiYvHwS7juGFva0NMrOEI54fly+B1RncF6sTZCzx+8pSkR8l8WK4M5y5d5emz5yQmJXPF7yZRMbFGzWuXTZuwzUWbsHeyY6f3Jo5e3cfGFduMPmoOL47VGfUSq47FJofH6pJlXfgn+THfr5nFumO/MWLaUKNOI3tBYW2DNi6jnrXxsSitDTMrnV1QOpXEYv5yLBb8iqpWxiCQwsYWy2XrKbHhD57u3i5Gzd9jBaFz/i0QLMuyG+AFVADqAW5AbUmSXgzZlAeWAtWBisBnQBNgAjAl0/aqA52AhsAMSZKy9BQkSRoqSZKvJEm+0Y8jX178r7I765dzMYLVvV5vBnX8kplffc+Y70biXDr3HZm8kF3mq5u9+LXZOLzn/U6TrzMuq0f6B7O67STWd5lOoxFdUBZS5XmenNRrtmWQmTRlFL+t2Mjjx08MllkWt6BDx9bUrtaKqh80oUiRInzS+93Nw3tVu2javTllq5Xn4CrD6TXF7Urw9ZKx/DphWa7aVE69cR1nKlOpUgVmfz+JUV9PBSApKZkxo6ezactyjp/YxYMHEWjS0t5htlev+za/q507t+PPC765ntLy6pw5q+foqBhqVmlJq6bdmT51Hr+tXUQx86L6Mr0+HkLVD5pQqJApTZs3yHW2HHnLfRyAjZ01P/wygxljvjdKWy7osquTl9vEhBED8fX/i56DR+Prfwt7W2uUSiWN69WkaYM69B3xDRNnL6BGlYoolUrjBs5utDsXP9foyBh6t+pP14a9ce/VASubrINDeS67AfocZlaaKKleryor5qxiaMcROJZypEOv9q9f8W29Yh9nGE6J0smF5Cmj+WfhbIp+PRGpaDEAtHGxPBo1iMShn1G49UdIxd9BPRuZVjLuV34pCJ3zzNqlf/kB19B1wiukLwuRZfmmLMta4BZwUtbt4W4Crpm2cUCW5aeyLMcBp9B19A3IsrxaluU6sizXsS+au85xjDoWOyc7/Ws7R1vionN+dvqibORDNdcu+PNB1fK5+vw38XdUAuaOGaO4Fo5W/BP96k7HrYMX+KBd1hs/4+9FkvL0OXYfuGSz1tuJjIzCySVjVNPJyUE/jSJzGWcXRwCUSiUWFuYkJiRRq04NZs6eyLWb3gwb3p8xE75k8NC+NG/RiAcPwomPTyQtLY3Dh45Tt37NPM8OkBAVj7VjxgiHlaM1CdEJWcpVa1yDj0d+wvwhc0lLyejEmhUzY/KG6exYuJUgv0CjZIyIUOPi7Kh/7ezsgFod/VKZKH0ZpVKJpYU5CQm6tuLk7MD231cxdMh4QkIe6tc56nGSls2707plD4KC7nPvXmjus4WrKZlpxN3F2TFrtkxllEollpYWJCQkEhGRzbqR0Tna5qv07tXljaa0AERGROHsnKktO9tnmU6ljozC2fmltpyYREpKqv6E4Ib/LUJDHlKufBmDdZ8/T8HTw5sOHY1zs2V0ZAz2mfZx9o52xEblfKSzaLEirNi6iF/mr+bGtVvGiFjg2dvaGNzgGR0bh62NlUEZOxtrls6dwu51Sxn9he4GTPNiuhO1Yf16sWf9UtYunoOMTOk3uFqVGzFv2SZeiI2OI/huCLUa1MjLeNl/ljoOO6eM0X3bXByrY9SxBP11D/VDNRqNFp9j5/mgWoXXr/iWtHGxKGwy6llhbYs2IS5LmZRLPqDRoI2OQhsRhsLJ8JgsJ8ST9jAUVeXqRs8svJmC1jmXgB9lWXZL/yovy/K69GWZJ3xpM73WYnjj68vnmXk6bHPHP4CSZZxxLOmAicqENl1bce54zm5+M7cshspUN+psWcKC6nWrEhL44DVrvb3I6/exKuOAZUlbFColld0bEOhlOKWihGvGZfcKrdxIDNVdlrcsaYuk1DUjC2cbrMs6khSe95dQ/a7epGxZV0qVdkGlUtG9Ryc8PU4alPH08OZ/n3YHoEu3jzh35gIA7h99Rq1qrahVrRWrVm7i54W/sW71VsLDI6lT1w0zs8IANGvekMC79/M8O8C960E4lnHErqQdJioTGrs3xdfrskEZ1yplGPrjcOYPnkty/CP9+yYqEyaunsyZPae46JG7Gylz4+rVG5Qr70rp9Dru2dMdjyMnDMp4eJygT1/dk2a6d+/AmfQ6trQ0Z8+e9cya8RMXLxq2HVtb3Ylf8eIWfDG0L5s27sx1tiu+/pQvXwZX15KoVCp69erKocPHDcocOnyczz/X3bjVo0cnTp0+r3+/V6+uujnwriUpX74Ml6/45Wib2bGwMKdZ0wYcPPhm00b8rt2kTLmMttzt4054engblPH08Kb3Z7q27N6tPT5nLwJgbV1Cf/m8tKsLZcu58iA0jKJFi2Bvr+toKJVK2rRrTlCgcdryLf87lC5bEudSjpioTPioWxtOH8/ZE0FMVCb8vGE+h/44itch79ev8P9U1YoVeBgeSXhkFKmpqRw9eY6WjesblElMStbP6V+zbTfdO+ruwdFoNCQ9SgbgbnAIgcGhNKprnEGHF275B1CqrAtO6W2ifbfWnD7uk6N17RxtKVRY95Qsc0tz3OpWI/Tew9es9fYC/ANwyXSsbt21JT45PFYH+N/FvLg5xa0sAajVuCah7+BYnRYUgNLJBYW9A5iYUKhZK1IvG07FTLnog0k13c9bsrBE4VQSbVQkCmtbMNXVs1S0GKpKVdFEhGX5jIJGPK0l//wNvLhT5xgwR5KkbbIs/yNJkjOQmsvtdZUk6UegKNAC3bSZPKPRaFk0bRk/b/8JhULB4Z1HCQkM5YsJA7lz/S4+Xn9SqcaHzFs3B3PLYjRp25Ah4wfSp9VAXCuUZtK8cWhlGYUksWX5DoM7x41F1mg5NmMjn26ehEKp4PquM8QFRdBsXA/UN0IIOnGNOv3bUaZJVbSpGp4mP+bgON0TK0rW+ZBGI9zRpmqQZS2e0zbwNDHvn7yg0Wj4duJs/ti3DoVSyfYtu7kbcI9vp47C/9pfeB71ZtvmP/h19QIu+3uRlPhI/3SLV7nme4NDB47hfW4/aWlp3Lxxh80bfs/z7ABajZZ1M1YzdfMsFEoFp3adJDwojN7jPiP4xj18T1zm8ykDKVzEjPG/fgNAXGQc84fMpWHnxlSqVwXz4ua07NkKgBUTlhF6OyRPM2o0GsaPm8n+g5tRKhVs2fwHd+4EMW36WK5du4nHkRNs2riTteuWcP3mKRITHzGg39cADPuyP2XLlWbS5K+ZNFn3Xlf3fsTGxvPTghlUq1YJgHk/LuPevdzn1mg0jB4zDY8j21EqFGzctJPbtwOZNXMCvlevc/iwF+s3/M6mjcsIuO1DYmISn/XV3Ud++3Ygu3cf4ub1U6RpNIwaPVXfqclumwAjvxrEhPEjcHCwxe/qCY56ejPsy4kAdOvaAa8TZ3ny5Gn2YXPwvUyeMJtde9eiUCrZsXUPdwPuMWnKKPz9/uLYUW+2bdmta8t+x0lMfMTQQbq23LBxXSZNGUVamgatVsOEsTNJSnyEra01W35fiampKUqlAp+zF9m43jhtWaPR8MOURazc8TNKpYL9Ow4TfDeEEd98wW3/O5w+7kMVt0r8vH4eFsXNad62CcMnDuHj5n1o36U1tRq4YVnCgi69OwIwffT33L0V9JpPNa6JM+dxxe8GSUnJtO7WlxGDP6eH+zuYpvAKJiZKpowZxrAJs9BotXTv2IbyZUqxfN02qnxYnpZN6nPF/yY/r9qMJEnUrlGFaWN1j4FMS9PQb+RkAIoVNWPetHGYmBh3WotGo2H+lCX8umMxCqWSAzsOc/9uCMO/GcJt/wDOHPehsltFFq//EYvi5jRr25gvJw6hZ/O+lKngyrhZI1/MQWPzyh3cCzDOiaVhZi1Lpv3Cou3zUSgUHNl5lNDABwyeMICA63c573WBijU+ZO667zC3LEajtg0ZNL4//VoNRqvVsmL2Kn7euRAkCLwZxKHtR4yeGa2Gx7/9jMV3C0Gh4PkJDzQPQzHrM4i0oABSL/9J6rXLqGrWxXLFJtBqebJhJfLfyZi41cF80Ah045EST/ftRPPA+PUsvBmpIMz3kyRpO7q54keBcGBI+qJ/gL6ABjgsy3LV9PIb01/vliTJ9cUySZJmAU5AOaAU8JMsy2v+7bMbOrd8/ysok84m+TNH/W0sSbyS3xFyrWWJSvkdIVeOxt7I7wi59iwtJb8j5FoJs2L5HSFXHM2sXl/oPXP1r235HSH3NLkdQ8pf9WoMen2h90wxZeH8jpAr+2vl/t6b94H1oTP5OBM7qx9L9zVqH23yg6358v0WhJFzZFl++Un7S7MpVjVT+QGZ/h2aeRkQKMty1r9gIwiCIAiCIAj5rEB0zgVBEARBEAQhM22+Po3ceP5fdc5lWZ6V3xkEQRAEQRAE4VX+X3XOBUEQBEEQhP+G9+PvrOe9gvYoRUEQBEEQBEH4zxIj54IgCIIgCEKB89+ccS5GzgVBEARBEAThvSFGzgVBEARBEIQCR8w5FwRBEARBEATBqMTIuSAIgiAIglDgaN+rv1ead0TnXBAEQRAEQShw/qt/hEhMaxEEQRAEQRCE94QYORcEQRAEQRAKnP/muLkYORcEQRAEQRCE94YYOX8NBQXrbgNlAcsLoJUL3rmvSQGrZ4VUsPICBayGCyZJ1PK7oVTld4JcufzXFhpVH5DfMXKliMI0vyPkiqRIy+8I/wn/1Ucpis65IAiCUHBoUvM7Qe4UsI65IAj5T3TOBUEQBEEQhAJHPK1FEARBEARBEASjEiPngiAIgiAIQoHz3xw3FyPngiAIgiAIgvDeECPngiAIgiAIQoHzX31aixg5FwRBEARBEIT3hBg5FwRBEARBEAoc8bQWQRAEQRAEQRCMSoycC4IgCIIgCAXOf3PcXIycC4IgCIIgCMJ7Q4ycC4IgCIIgCAWOeFqLIAiCIAiCIAhGJUbOBUEQBEEQhAJH/o/OOhcj50ZQv0VddpzdxE6fLfT96tMsy2vUr856z1WceeBFi07NDJadfejFxuOr2Xh8NfM3fP+uIlOmeXW+8F7AsDOLaDDcPctytz6tGHTsRwZ6zKXP7ulYV3AyWG7hZM2422upN7Sj0TK2btOUS9eO4et/gtHjhmZZbmpqyrqNP+PrfwIv792ULOVssNzZxZGHan9GjhqckdvSnI1bfuHiVU8u+npSt56b0fLXaF6TRd4rWHJmJV2Gf5xlecchXVhw4hfme/7M1O2zsXG2BcDG2Za5hxfxo8cSFngto02f9kbLCNCmbTOu+p3A/4Y3Y8d/mWW5qakpGzYtw/+GN96n91IqvZ5r166Oz4XD+Fw4zPmLR+js3g6AQoVMOXVmH+cvHuHSFU+mTB3z1hnbtWvBX3+d5c5tHyZO/CrbjNu2reTObR/O+xyidGkX/bJvvhnJnds+/PXXWdq2bQ7ABx+Uw/fKcf1XfFwAo74eAsD06eMIDfHVL/voo1Zvnb9V66Zc8PXkst9xRo39Ipv8KtZsWMJlv+N4ntylb8slSznzMOo6p87t59S5/SxY8h0AZmaF2b5rFX9eOcq5i4eZPmv8W2f8N41a1ueAzw4OXdjFoJGfZ1leq4Ebvx/fwNXws7Tp3FL//odVKrD58Gr2ntnKH96bad+1tVFzvuBz6Sqd+wynw6dDWbt1d5blkVExDB4zje4DvmbAqClExcTply1euZFu/UfSrf9Ijp48907yvs60HxbTrNP/6NY36+9nfmnYoh67z21l7/nt9B/ZJ8vymvVrsOXYWi489KZVp+ZZlhctVoQjV/cwce7b7x9yqk6L2qw9vYYN59bRa8QnWZZXrV+V5R6/4BFymCYdmxgsGzxlEKtP/MYa71UM/+7d/RxUNeth+esWLH/bRuEen2VbxrRxSyyXb8Lil40UHTcdAIWtPRaLVmOxZC0Wv2yk0Edd3llmIffe+5FzSZKmyLL8Q37nyCmFQsH4uaMZ8+lEYtSxrPVYic/xPwkNeqAvEx0Rzdyx8/n0y15Z1n/+LIUB7bJ2PI1JUki0m9Of3/vM4++oBAYcnE3QiavEB0Xqy9w+cAH/bd4AlG9Ti9bT+rKr/0/65a1n9OH+6etGy6hQKPhp0Sw+7jqAyIgoTp7Zg+cRb+7evacv07dfT5KSkqnj1oaPe3Ri1uyJDB6QsaP/Yd5UTnqdNdjujz9N4+SJswz4/GtUKhVmRQobJb+kUDBwzjB+6DOT+Kh45h5cwNUTl4kICteXCb11n6mdx5PyLIU2fT/is8n9WTZyIYkxicz8eBJpKWkUKlKYBceXcdXrMokxiXmeU6FQsGjxd3R170dERBSnz+3H48gJ7gZk1HO//r1ISkrGrXorevTszHdzJjGw/yhu3w6keZOuaDQa7B1s+fPiEY56nOT58xQ6d+zD48dPMDEx4fiJXXgdP82VK/5vnHHZ0rl06Pgp4eFqLl7w4PDh49y5E6QvM2jgpyQlPqJS5Sb06tWFH36YSp8+w6lUqQK9e3WlhlsrnJzs8Tz6O5WrNCUwMJg6ddvpt/8g9Cr7DxzVb2/psjUsWbLqDWs1a/55i2bwSbeBREZEc/zUbjw9vAm8G6wv06ffJyQlJVOvZju69ejIjO8m8MXAsQCEhjykZdNuWba74pf1nD93CZVKxd6DG2ndphknT5zNUi4v8k/5cQLDeo0mWh3Dds91nD5+jvuBofoyURFRTB/9Pf1HGHYenj19xrSvZ/MwJBxbext2HF/Pn6cu8XfyP3me8wWNRsP3S1axZvFsHGyt6T10PC2b1KOcayl9mYW/rqdL+5Z07dCaS1ev8/PqzcybNo4zF65wOyiY3euWkpKayoBRU2jaoDbFihYxWt6c6NaxLZ/16MKUOQvzNccLCoWCb34Yy8j/jSNaHcsmj9WcPeZDSKbjXlRENN+N+YG+X/4v2218+c0Qrl18s33Cm1AoFHz1/VdM/mwKceo4fjm8lItel3gY9FBfJjYihkXjFtFzWA+DdSvXrkSVOpX5st0IABbtXUj1BtW4cfGmsUNTZNgY/p45Hm18LBYLV5Fy+TzasIx6Vjg6U7hnH5InfYX8+B8ky+IAaBPjSZ70FaSlQmEzLJdtIOXyeeSEeONmNjIx5zz/TMnvALlRqWZFwkMjiHyoJi01jZMHvGnavpFBmajwaILv3EfWvh/NytGtHImh0TwKi0WbquH2oYtUaFvboEzKP0/1/1YVKWRwKalCu9okPYwlLjDCaBlr16lOyP0HPAgNIzU1lb17jtChs+GoW8dObfh9+14ADuz3pFmLhhnLOrchNDSMgEwdOHPzYjRqVJctm/4AIDU1leRHfxslf3m3CkSFqokJi0aTmsaFQz7UaVvfoMztC3+R8iwFgHt+d7FytAZAk5pGWkoaACpTFZJCMkpGgDp1anD//gNC0+t5z+7DdOrc1qBMp85t2LFtDwD79x2lRQtd+3769BkajQaAwoUKIWe62vj48RNdfpUJJioTZPnNL0XWq1uT4OBQQkIekpqays5dB3B3N7ya4O7eji1bdD/XPXuO0Kplk/T327Nz1wFSUlIIDQ0jODiUenVrGqzbqlUT7t9/wMOHxmnPtWpXJ/T+Ax6EhpOamsr+vUfo0MmwLXfo2Iqd2/cBcGj/MZo2b5jdpvSePn3G+XOXAF07vnH9No7O9kbJX7VmZcKv0i0KAAAgAElEQVRCwol4GElaahqe+0/Qon1TgzKRYVEE3QlG+9I+7sH9MB6G6E5IY6PjSIhLpIR1caPkfOHmnSBKOTtS0skBlUpFh9ZN8fa5ZFAmODSM+rVrAFCvVnVOpS8PDg2jbo2qmJgoKWJWmA/LueJz6ZpR8+ZEHbdqWFqY53cMvSo1KxEWGkFE+nHP68BJmrc3HGlWh0dx7859ZG3W3/2K1T7AyrYEl85ceVeR+dDtAyJDI4l6GEVaahqnD56hYbsGBmWiw2MICQhF+9L+SpZlTAuZYmJqgspUhYlKSWJcktEzm1SohDYqAm20GtLSSDnnjWk9w3ou1M6d5x77kB/rTnjlR+m50tJ0HXNAUqlAURC6f/9/vVc/HUmS9kuSdFWSpFuSJA2VJGkeYCZJkr8kSdvSy/SVJOly+nurJElSpr//jyRJ89PXPyFJUj1Jkk5LknRfkqQu6WUGSJJ0QJIkT0mS7kqSNDOvvwdbBxtiImP0r2PUcdg62OZ4fdNCpqzzWMnqQ8tp2r5xXsfLlrlDCf5WJ+hf/61OwNyhRJZytfq1YdjZRbSc/D9OzNwMgMqsEA2Gd8bn571Gzejo6EBEhFr/OjIiCkdHw86Ho5M9EeFRgG60LPnRP1hZl6BIETNGjx3KTz/+YlC+tGtJ4uISWP7bfE77HGDp8rkUKWJmlPwlHKyIV2dcKo9Xx1PCweqV5Vv0bsP10xmdACtHG+Z7/szyi2s5+Nteo4yaAzg6ORAenrme1ThlU88vymg0GpKT/8bKWtde6tSpwaUrnly4fJQxo6bpO+sKhQKfC4cJDr3CKe/z+Pq++VUWJ2cHwsMzrupERKhxdnLIUiYsvYxGo+HRo2SsrUvg7JR1XSdnw3V79+rKzp37Dd4bMXwg1656sWb1IooXt3zj7JDeTiOi9K8jI6KztGUHR3t9e9fXsZWujkuVdsH73D4OHNlCg4aGJ9Ggm6rVrkNLzp258FY5X8XO0ZaoyGj96xh1LPaOOd/HvVC1ZiVUKhVhocY7qQeIiYvHwc5G/9re1oaYWMPRwg/Ll8HrzJ8AnDh7gcdPnpL0KJkPy5Xh3KWrPH32nMSkZK743SQqJtaoeQsiWwcbojMd96LVsdjmsE1IksSYmV+xbM5KY8XLlrWDDbGRGT/LOHUcNg7WOVr3zrUArl+4wQ7fbey4uo2rZ64Rdi/MWFH1JGsbNHEZ9ayNj0VhbWNQRunkgsKpJObzlmPx06+oatbTL1PY2GKxdD3F1/3Bs73bC/yoOej+Qqgxv/LLe9U5BwbJslwbqAOMAhYAT2VZdpNluY8kSZWA3kBjWZbdAA3wYnJbUeB0+vp/A98DbYHuwOxMn1EvfR034BNJkuq8HCL9xMBXkiTfqMeRLy/+V5KUdVQzN6OEPer9j8EdhzPrq7mM/u4rnEs7vX6lt5bNSGw2ka9tPsGqZuM5Pe93Gn2tu6zeZNzHXFnrSeqT58ZNmF3El+r1VXX/7dRRrFy+QT96+4KJiZIablXYsHY7LZp05cnjp4wZNyxPc+uz5bCOAZp0b07ZauU5tGqf/r0EdRyTPhrD2GZf0qxHSyxt3q6D+MqcOannbL8XXRlf3+vUr/sRLZp1Y/yE4RQqZAqAVqulScPOVPqgEbVrV6dS5Q/eIuPrf8eyL/P6dVUqFZ07t2P3nsP691at2syHFRtRu0471FExLPhpxhtnf3W2nLXl6KgYalZpSaum3Zk+dR6/rV1EMfOi+jJKpZLV6xaz9rctPAgNz7KNvJCTNvI6NnbWzP1lBjPGzH2rqyg5kd32X67fCSMG4uv/Fz0Hj8bX/xb2ttYolUoa16tJ0wZ16DviGybOXkCNKhVRKpVGzVsQvc1xr+eA7pz3vmjQuX8Xsm/HOVvXydWRkuVL0qfe53xWty81GtWgav2qeRswWzk4jiiVKJ1c+HvqaP5ZOJuiIyciFS0GgDYuluTRg0j68jMKtfwIyTLrIFxBIxv5K7+8b3POR0mS1D393yWBCi8tbw3UBq6k7wzMgBe/0SmAZ/q/bwLPZVlOlSTpJuCaaRtesizHA0iStBdoAvhm/hBZllcDqwEaO7fK1c8nRh2LnZOd/rWdow1x0XH/soahuGjdmWzkQzV+F/ypULU8EQ9yd4KQW39HJWDumDGKa+5oxd/Rrx6ZvX3wIu2+HwiAk1t5KnaoR8vJ/6OQRRFkWSbteSrXNnnlacbIyCicnR31r52cHYiKMtyZR0ZE4eziQGRkFEqlEgvLYiQmJFG7Tg26dP2IWXO+wdLSAq1Wy7Nnzzm435PIiCiupo/iHjjgabTOeUJUPNaOGSMc1o7WJEYnZClXtXF1uo3syexe0/RTWTJLjEkkPDCMD+tV5rJH3o+MRkZE4eKSuZ4dUb9cz5G6Mvp6tjAnIcHwkm7g3WAeP35C5cof4ueXMQ/z0aO/8Tl3iTZtm3HnduAbZYwIV+PiknHS6uzsSKQ6OkuZki5ORESoUSqVWFpakJCQSHhE1nXVmUaBP/qoJX5+N4nJdENg5n+vW7eN/fs3vVHuFyIjonDONFrv5GyfpS2r09u7OjJaX8eJibo6TknR/f+G/y1CQx5SrnwZrvv9BcDipXO4HxzKqpVvl/HfREfG4uCUMdJv52hLTFTO93FFixVh+daFLJ+/mpvXbhkjogF7WxuDGzyjY+OwtTG8amVnY83SuboZlE+ePOXE2T8xL6Y76RnWrxfD+unuD/pm9kJKu7yLAZOCJUYdi32m4569oy1xOWwT1WtXwa1+dXr270aRomaYqFQ8ffyU5T/kzT0erxKnjsPWKWN038bRhvjonI0kN2rfiAC/AJ49eQaA7ylfKtWsyF+X/jJK1hfk+FiUNhn1rLC2RZtgWM/a+FjS7t4GjQZtTBSaiDAUji5o7gVkbCchHk1YKCZVqpP65xmjZhbezHszci5JUgugDdBQluUagB/w8t15ErApfSTdTZblD2VZnpW+LFXOOFXXAs8BZFnWYngS8nJnO09PjgL8A3Ap44xjSQdMVCa07toKn+M560SZWxZDZaoCwLKEBdXqViU08MFr1np76uv3sSrjgGVJWxQqJZXdG3DPy3BeZQnXjINx+VZuJIbqLstv+2QOK5uMZWWTsfiuP8aFFQfzvGMOcO3qTcqWc6VUaRdUKhUf9+iE55GTBmWOepzkf5/pnoLStdtHnDtzEYBO7T/DrWpL3Kq25LdfN7Jk0W+sXb2VmJg4IiLUlK9QBoDmzRsa3PiYl4KvB+FQxhHbknYoVSY0dG/CVa/LBmVcq5RhyI8jWDj4B5LjH+nft3KwRpU+Al3Uoigf1qmIOtg4J2xXr96gbDlXSqfXc4+enfE4csKgjMeRk3zaR3eDVLfuHTiTPn2idGkX/ahiyZJOVPigLA8ehmNtY4WlpW5+bOHChWjRsjFBd++/ccYrvv6UL18GV9eSqFQqevfqyuHDxw3KHD58nM8/1z19oUePTpw6fV7/fu9eXTE1NcXVtSTly5fh8hU//Xq9e3fLMqXFwSHjYNitawdu3br7xtkB/K7dpEymttzt4054engblPH08Kb3Z7pxCvdu7fE5q2vL1tYlUKTPFS3t6kLZcq48CNVdTp88bQwWlsWY+q1x75+/5X+HUmVdcC7liInKhI+6teHMcZ8crWuiMmHJhnkc+uMoXodOGTXnC1UrVuBheCThkVGkpqZy9OQ5WjY2vN8jMSlZPz9+zbbddO/YBtBNKUp6lAzA3eAQAoNDafTSPQoC3PYPoFQZF5xK6tpE266tOXv8fI7WnT5yDu51P6Fr/d4snf0rHruPGb1jDnD3eiDOrk7Yl7THRGVCiy7Nueh1MUfrxkbGUr1+NRRKBUoTJdUaVOPhO5jWkhYUgMLRBYWdA5iYYNq0FamXDes59aIPqmq6NiqZW6JwLok2OhLJ2hZMdccRqWgxTCpWRRth/MzG9l+d1vI+jZxbAomyLD+RJKki8OLOjFRJklSyLKcCJ4EDkiQtkWU5RpIkK8BcluXc9GDbpq/3FOgGDMrLb0Kj0bJk2i8s3j4fpULJ4Z1HCQkMZciEAQRcD8TH608q1viQH9fNxtyyGI3bNmTI+AH0bTWI0hVK8828sWhlGYUksXX5DoOnvBiLrNFyfMYmem/+Bkmp4MauM8QFRdB0XA/UN0K4d+Iatfu3o3STKmhTNTxLfsyRccbfeWam0Wj4ZsJ37N6/HqVCybYtuwkIuMfkqaPx87uJp4c3Wzf/wW9rFuLrf4LExCSGpD/d4t9MmjCHVWsXYWqqIjQ0jJHDvzVKfq1Gy8YZa5i8eSYKpZLTu04QHhRGz3GfEnLjHldPXOGzKQMoXKQwo3/9BoD4yFgWDvkB5/Iu9J02EFmWkSSJw6sPEHbXOO1Co9Ewcfws9h3YhFKpYMvmPwi4E8TUaWO4du0mRz1OsnnTTlavXYz/DW8SEx8xsP8oABo2qsPYcV+SmpaGVqtl3JgZJMQnUqVqRX5bvQClUolCIbFvjweent6vSfLvGUePmcaRI9tRKhRs3LST27cDmTlzAlevXufwYS/Wb/idjRuXcee2D4mJSfTpq3uqwu3bgfyx+xA3rp8iTaNh1Oip+k6ZmVlh2rRuxogRkww+b96P06hRozKyLBP6IDzL8jfJP3nCbHbtXYtCqWTH1j3cDbjHpCmj8Pf7i2NHvdm2ZTe/rl7AZb/jJCY+YuggXVtu2Lguk6aMIi1Ng1arYcLYmSQlPsLRyZ5xE4cTeDcY77O66VDr1mxl6+asjw18WxqNhh+nLGbljiUolEr27zhM8N0QRnwzhFv+AZw57kMVt0osWf8jFsXNad62CSMmDubj5n1p36U1tRq4YVnCgi69dY9dnTF6LndvBb3mU9+ciYmSKWOGMWzCLDRaLd07tqF8mVIsX7eNKh+Wp2WT+lzxv8nPqzYjSRK1a1Rh2ljdo/HS0jT0GzkZgGJFzZg3bRwmJvk/rWXizHlc8btBUlIyrbv1ZcTgz+nhbtxHrP4bjUbDT1N/Ztn2hSiVCg7+7sH9wFCGTRzEnet3OXv8PJVrVOSndd9jUdycJm0bMWzCIHq37J9vmbUaLSumr+SHrd+jUCo5vvM4DwIf0m/85wTeCOSi1yU+qPEBM9ZMx9yyGA3a1KffuL4MbfMl5474UKNRDVZ5rUSWwfeML5dOXHr9h759aJ6s/hnzWQtBoeD5SQ80YaGYfTaItHsBpF7+k1S/y6hq1sVy+SZkjZanG1ci/52MSY06FBk04sX8Pp7t34nmwZsPkgjGJRl7vl9OSZJUCNgPOAN3AVtgFtAB6AJcS5933huYjG7UPxX4Spbli5Ik/SPLcrH0bc0C/pFleWH6639kWS4mSdIAoCO6+enlge2yLH/3b7lyO60lv7mbFLxLrj8lvIOdWh5rb1UlvyPkypG4G/kdIdeephr3PgZjKG5WLL8j5IqTWc5ugHuf+F7fkN8Rckepyu8Eb6RR9QH5HSFXrJRFX1/oPbKj1pPXF3oPWR04Y7zHhb2BL1w/MWofbU3oH/ny/b43I+eyLD9H1xF/2WlgUqZyO4Gd2axfLNO/Z71qGRAjy/LIt4wrCIIgCIIgCHnuvemcC4IgCIIgCEJOyfn6TBXj+X/VOZdleSOwMZ9jCIIgCIIgCEK2/l91zgVBEARBEIT/hvfj76znvffmUYqCIAiCIAiC8P+dGDkXBEEQBEEQCpz/6pxzMXIuCIIgCIIgCO8JMXIuCIIgCIIgFDhizrkgCIIgCIIgCEYlRs4FQRAEQRCEAkf7nvyV+7wmRs4FQRAEQRAE4T0hRs4FQRAEQRCEAue/OW4uRs4FQRAEQRAE4b0hRs5f42r8vfyOkCs2dkXyO0KuORaxyu8IuZZWwM7XUzRp+R0h14qbFcvvCLn2JPV5fkfIFc9qhfI7Qq7VqzEovyPkyuW/tuR3hDfy542N+R0h14bWmZjfEXKswZ+x+R3hjQTmd4CXaAvYsTinROdcEARBEIykUfUB+R0h1wpix1wQ/ktE51wQBEEQBEEocP6rfyFUdM4FQRAEQRCEAkf8ESJBEARBEARBEIxKjJwLgiAIgiAIBc5/9YZQMXIuCIIgCIIgCO8JMXIuCIIgCIIgFDj/1RtCxci5IAiCIAiCILwnxMi5IAiCIAiCUOCIp7UIgiAIgiAIgmBUYuRcEARBEARBKHBkWcw5FwRBEARBEATBiETnXBAEQRAEQShwtMhG/coJSZI+kiTpriRJ9yRJ+jab5eMkSbotSdINSZJOSpJU+nXbFJ1zQRAEQRAEQcglSZKUwAqgA1AZ+FSSpMovFfMD6siyXB3YDfz0uu2Kznkeadu2OTdunOLWrbNMmDAiy3JTU1O2bFnBrVtnOXv2AKVLuwBgZVWcY8d+Jy7uDkuWzDZYR6VSsWLFPG7ePM31695069bBaPlrNq/Fr6d+47ezq+kxomeW5V2GdGP5yV9ZeuwXZu+Yi62zrX7ZzM3fse3m70zbMMNo+V7WpGUDDp/fxdGLuxnydb8sy2s3cOMPr01cjzhPu86tDJat2vEzFwJPsGLroncVFwC35jVZ6v0rv5z5jW7De2RZ3nlIF5acWM5Cz6XM2D4bm/Q6dq1chrn75rPY6xcWei6lUecmRstYENtxq9ZNueDryWW/44wa+0U2mVWs2bCEy37H8Ty5i5KlnAEoWcqZh1HXOXVuP6fO7WfBku8AMDMrzPZdq/jzylHOXTzM9Fnj8zQv6OrZz/8kN26eZvz44dlkNmXT5uXcuHma02f2U6qUrp5btWqCz/lDXL7sic/5QzRv3jDLurv+WMOVK8fyPHNmherXxXb7Jmx/30rRvp9mWW7WoT12h/Zhs2ENNhvWYNa5o36Z+fCh2Gxej83m9RRu1dKoOV9o1LI++3x2cODCTgaO7Jtlea0GNdh+fD1Xws/QpnML/fuOLvZsO7aO309sZPeZrfTs1+2d5AVo2KIeu89tZe/57fQf2SfL8pr1a7Dl2FouPPSmVafmWZYXLVaEI1f3MHHumHcR97Wm/bCYZp3+R7e+X+Z3FL2qzd344eQy5p1eTsfh3bMsbzfYne+9fmb20cVM3DYT60zHvXXBu/jOYyHfeSxk1Josg6XvRNNWDfG8sAevy/sYOqp/luV1GtZk38mt3FZfpL1763xIaHxaI3/lQD3gnizL92VZTgF+B7pmLiDL8ilZlp+kv7wIuLxuowXuhlBJklyBw7IsV83nKHoKhYKlS7+nU6c+hIerOX/+EIcPexEQEKQvM2BAb5KSHlGlSjM++cSd77+fzOeff8WzZ8/57rtFVK78IVWqfGCw3W+//ZrY2DiqVWuBJElYWRU3Wv5h3w9nZp9pxKvjWXhoCZe9LhEWFKYvE3IrmHGdxpLy7Dkf9e3AgCkDWfCV7uRv36q9FDIrRPs+HxklX3Z5p86byBe9viY6MoadxzZy6tg5ggND9GXUEdFMHT2HAcOzHtTW/7oVM7PCfNIv687YmJkHzxnGnD4zSYiK58eDC/E9cZlwgzoOYVLncaQ8S6Fd34/4fPIAloxcwPOnz/ll7M9EhaopYWfF/COL8D/rx5Pkx3mesaC1Y4VCwbxFM/ik20AiI6I5fmo3nh7eBN4N1pfp0+8TkpKSqVezHd16dGTGdxP4YuBYAEJDHtKyadYO14pf1nP+3CVUKhV7D26kdZtmnDxxNs8yL14yG/fOfYmIiOLcuYMcOeJFQMA9fZn+A3qRlPSI6tVa0LOnO3O+/5b+/UYSH59Iz56DiVLHULnyBxw4uJkK5Rvo1+vStT2P/3mS3cfmHYUCi3GjSRg7EU1MLDZrf+O5z5+khT4wKPbM+xTJS5YZvFeoYQNUH1QgbuAQJJUpVst/5vnFS8hPjJdZoVDw7Y/jGd5rDNHqGLZ5ruXMcR/uB4bqy6gjopk5ei79RhieaMRGxzPA/UtSU1IxK2LG7jNbOHPMh9joOKPlfZH5mx/GMvJ/44hWx7LJYzVnj/kQEpRRx1ER0Xw35gf6fvm/bLfx5TdDuHbR36g5c6Nbx7Z81qMLU+YszO8oAEgKBZ/P/oKFfWeTEBXPjIPz8fe6QuS9cH2Zh7dDmO3+DSnPUmjZtz29Jn/OypGLAUh5lsLMjhPyKz4KhYKZ8yYx8JOviIqMZs/xzZz0PGt4HAyP4tuvZzF4xOf5lrOgkyRpKDA001urZVlenem1MxCW6XU4UP9fNjkYOPq6zxUj53mgbl03goNDCQl5SGpqKn/8cQh393YGZdzd27F1624A9u71oGXLxgA8efKUP/+8wvPnz7Jst3//Xvz00wpAd0dyfHyiUfJXcPuAqFA10Q+jSUtN49yhs9Rr18CgzM0LN0l59hyAu353sXa00S+7cf46T/95apRs2alWqzJhIeGEP4gkNTUNj/1etPyomUGZyDA1gbfvIWuznvteOudr/A7MS8q7VSAqNIqYMF0dnz90jjpt6xmUuXXhJinPUgAI9LuLlaM1AOqQSKJC1QAkxiTwKO4RFlYWeZ6xILbjWrWrE3r/AQ9Cw0lNTWX/3iN06GQ4QtShYyt2bt8HwKH9x2iazWhzZk+fPuP8uUsApKamcuP6bRyd7fMsc506btwPfkBoaBipqans3n2Izp0N67lzp3Zs27oHgH37PGjRohEA16/fIkodA8Dt24EUKlQIU1NTAIoWLcLXXw9h/vxf8ixrdlSVKqIJj0QTqYa0NJ6e8KZQk8Y5WtfEtTQp/tdBo0V+9oy0e8EUalDv9Su+hao1KxEWEk7Ew0jSUtM4tv8kLdo3NSijDosi6E4wWq3hHNO01DRSU1IBMC2kQpIko2Z9oUrNSoSFRhDxUE1aahpeB07SvL3hFTN1eBT37txH1madF1ux2gdY2Zbg0pkr7yRvTtRxq4alhXl+x9Ar61aemAdRxIZFo0lN4/IhH2q2q2tQJuDCX/p9crBfICUcrPMjaraq16rCg9Awwh5EkJqaxpH9x2nTwfAKSkSYmru376GV/6tPA9f9hVCj/ifLq2VZrpPpa/VLEbLbKWQ7WV2SpL5AHWDB676vgto5V0qStEaSpFuSJB2XJMlMkqTTkiTVAZAkyUaSpND0fw+QJGm/JEmHJEkKkSRpZPrkfD9Jki5KkmT1tmGcnBwID4/Uv46IUOPkZP/KMhqNhuTkv7G2LvHKbVpa6jpfM2dO4MKFI2zbthI7O5tXln8b1g7WxEXG6l/Hq+Owtn/1Tqht73ZcPXXVKFlywt7BDnVktP51dGQM9g62/7JG/rNysCZenTHalqCOx/pfdvSte7fF73TWOi5fowImpiZEP4jK84wFsR07OtkTEZFRF5ER0Tg6GmZ2cLQnIkJtkNnKSpe5VGkXvM/t48CRLTRoWDvL9i0szWnXoSXnzlzIs8xOTvaERxjWs2OWes4o86p67tatAzeu3yIlRdd5mDFjPMuWreXJk6wnSHlJaWuDJiZG/1obG4vSNuvPtHDzZthsXEvxObNQ2Ol+P1PvBVOofn0oVAjJ0gLTWm4o7Yz7u2vnaEt0ZEbeaHUMto45/0x7Jzt2em/i6NV9bFyxzeij5gC2DjYvZY7NcWZJkhgz8yuWzVlprHj/CSXsrUiIzLxPTqDEvxz3mvVqzc3T1/SvVYVMmXFwPtP2/UjNdsY9wcyOvaMdUREZx8GoyBjsHe3eeQ6BcKBkptcuQOTLhSRJagNMBbrIsvz8dRstqJ3zCsAKWZarAElA1gm8hqoCn6GbGzQXeCLLck3gApB1wnIuZTea8vKzN3NSJjMTEyUuLk5cuOBLw4aduHTpKvPmTXvbqNnL5rzvVdmad29B+erl2bdqj3Gy5ER2eXN4V/X75FV13LR7c8pWK8/BVfsM3i9uV4Kvl4zl1wnLjPJs14LYjt8mc3RUDDWrtKRV0+5MnzqP39Yuoph5UX0ZpVLJ6nWLWfvbFh6EhmfZhjEz85oylSpVYM733/L111MAqF69MmXLlebQQePONX9VNl7K/+z8BWI++ZS4AUNI8b1K8am6ObkpV3x5fvEiNr8tp8Ss6aT+dRtZY+RRvRzk/TfRkTH0btWfrg17496rA1Y2rz4ZzSu5/T3LrOeA7pz3vmjQuReykYs6btitGa7Vy3F09QH9exMaDWN2l0msGvUzn80YiG2pvLu6lhPZN+uCdxx8W+/B01quABUkSSojSZIp8D/gYOYCkiTVBFah65jn6BezoHbOQ2RZfjGZ7irg+pryp2RZ/luW5VjgEXAo/f2b2a0rSdJQSZJ8JUny1Wj+eW2YiAg1Li5O+tfOzo6o1TGvLKNUKrGwMCchIemV24yPT+Tx4yccOOAJwN69R3BzM840+3h1PDZOGaMy1o42JMQkZClXo0kNPhnZm7mD55CWkmaULDkRrY4xGGm0d7IjJsr4o1lvIyEq3mAqkJWjNQnRWeu4WuMafDzyE+YPmWtQx2bFzJi8YTo7Fm4lyC/QKBkLYjuOjIjC2dlB/9rJ2Z6oKMPM6sgonJ0dDTInJiaRkpJKYqIu+w3/W4SGPKRc+TL69RYvncP94FBWrdyUZ3kBIiKicHE2rOeol+o5MlOZl+vZydmBHb+v4osh4wgJeQhAvfq1qFmzGrfv+HDi5B+Ur1CGo56/52nuFzQxsSjtMkboFLa2aOLiDcrIycmQqpsO8uTQEVQfZtyH8M/mbcQN/IKEsRNBktCE5d2JT3ZiImOwd8rIa+9oR+wb7C9io+MIvhtCrQY18jJetmLUsS9ltiUuh5mr165Cr4Efc+DSTkbPGEHHnu0ZOWWYsaIWWIlR8Vg5Zd4nW5GUzXGvcuPqdB7Zg6VDfjTYJyfF6KbnxYZFE3DxFqWrlMmyrjFFRcbgkGm6nYOTHTFRsf+yhmAMsiynASOBY8AdYJcsy7ckSZotSVKX9GILgGLAH5Ik+UuSdPAVm9MrqJ3zzJcENOhubE0j4/sp/C/ltZlea8nmptjMc4yUymKvDePre53y5ePZP7AAACAASURBVMvg6loSlUrFJ5+4c/iwl0GZw4e96NtX9xSUjz/uyOnTf752u0eOnNA/jaFly8bcuRP0mjXeTND1QBzLOGFX0h4TlQlN3Ztx2euSQZkyVcoy/MeRzB08h0fxj4ySI6f+8rtDqbIlcS7liEplQsdubTl1LG9u1jOWe9eDcCzjiF1JO0xUJjR2b4qv12WDMq5VyjD0x+HMHzyX5Ex1bKIyYeLqyZzZc4qLHq9vN2+qILZjv2s3KVPOlVKlXVCpVHT7uBOeHt4GZTw9vOn9me7mX/du7fE5exEAa+sSKBS6XUZpVxfKlnPlQajuvp7J08ZgYVmMqd/+kGdZX7h69TrlyrtSOj1zz57uHDliWM9HPLzo01d3QbB7946cOaOrZ0tLC/bu2cDMGT9x8WLGtKe1a7ZSvlx9KldqQpvWn3AvKIQOH2V/o+DbSg0IQFnSGaWjA5iYYNamFc/PG7YDhXXGbMFCTRqR9uBh+gIFkoVuqpNJubKYlCvL8yvGnRd9yz+AUmVdcCrliInKhPbdWnP6uE+O1rVztKVQYd2cfnNLc9zqViP03kNjxgXgtn8Apcq44FRSl7lt19acPX4+R+tOHzkH97qf0LV+b5bO/hWP3cdY/sMqIycueEKu38PO1REbFzuUKhPquTfBz8vXoEypKmXo/8Mwlg2Zx9/xyfr3i1gUxcRU13UoVsKcCrUrEhlk3JPMl930u41rmZK4lHJCpTKhU7d2nPR8v4+DxiDLslG/cpjBQ5blD2RZLifL8tz092bIsnww/d9tZFm2l2XZLf2ry79vsQA+reVfhAK1gctA1mcBGpFGo2HMmOkcOrQFpVLJpk07uXMnkBkzxnH16k2OHPFi48adrF//M7dunSUhIYl+/Ubq17979zzm5uaYmqpwd29P5859CQgIYtq0H1m//mcWLJhJXFwCQ4fm/SPdALQaLaun/8asLbNRKBWc3OlFWOBDPhvXh3s3g7jsdZmBUwdhVqQw36zUXZ6Oi4xl7uA5APywez4u5VwoXLQw6y5tZPnEZfidvfZvH/lWNBoNcycvZPXvy1AoFezbcYjguyGM/GYot67f4dSxc1R1q8TSDT9hUdycFu2a8tXEL+jaXPckhs0HVlGmfGmKFDXjpN8hZoz9nvOnL73mU9+OVqNl3YzVTN08C4VSwaldJwkPCqP3uM8IvnEP3xOX+XzKQAoXMWP8r98AEBcZx/whc2nYuTGV6lXBvLg5LXvqHgu5YsIyQm+H/NtH5lpBbMcajYbJE2aza+9aFEolO7bu4W7APSZNGYW/318cO+rNti27+XX1Ai77HScx8RFDB+me1NKwcV0mTRlFWpoGrVbDhLEzSUp8hKOTPeMmDifwbjDeZ3VTi9at2crWzbvzLPP4cTM4cHAzSqWSzZt3cedOENOmj+XatZt4HDnBpo27WLtuMTduniYxMYn+/b4GYNiX/ShbrjTfTh7Ft5NHAdDF/XNiY+P/7SPzlkZL8uJlWC3+CRQKnh45SlpIKMUGDyQ14C7Pz/9J0Z4f624S1WjQJieTNHeebl0TJdYrlgIgP3lC0uy5YORpLRqNhvlTlvDrjsUolEoO7DjM/bshDP9mCLf9Azhz3IfKbhVZvP5HLIqb06xtY76cOISezftSpoIr42aN1E2DkSQ2r9zBvYD7Rs37IvNPU39m2faFKJUKDv7uwf3AUIZNHMSd63c5e/w8lWtU5Kd132NR3JwmbRsxbMIgerfM+ji998XEmfO44neDpKRkWnfry4jBn9PDvX2+5dFqtGybsZbxm6ejUCo4t8ubyKAwuo39H6E37+F/wpdek/tRqEhhRvyq22fFR8Sx7It5OJV3of8Pw9DKMgpJ4sjKfQZPeXkXNBoNsycvYN2uX1AqlOzecZB7d+8zatIw/vK/g/exs1Rzq8yKTQuwsLSgZbumjPpmKJ2a9n6nOYU3IxW0OUovP0pRkqQJ6C4X/A7sAv4BvIG+/8fefcdHUbxxHP88dwnSS+i9g/QqTaRJEZCi2JCm2FFREVBEaSrlp6KiiIAKiIoNBelNiiBI772XhJoEkJoyvz/2Eu7SSCSXvQvP2xcvsrtz4Zt1szs7OztjjCkhIk9gDf7+kqv8Ydfy2bjbEpIxYzG/2kGt8lW1O0KK7b/mf4/iKmYscPNCPmTWqU12R0ixbHdksjtCil2OuOl7Pj5lf820fRSfGtrsv253hBQJcDjtjpBif2+dbHeE/+TZ2v3sjpBsqy4duXkhH7T3zPq0GbIomVoVbe3VOtqCY/Ns+Xn9ruXcGHMY6wXPmGX3QVPda6Zvu7ZPBia7lS/h9rXHNqWUUkop5R/8cTCI5PDXPudKKaWUUkqlO37Xcq6UUkoppVQyhzv0O9pyrpRSSimllI/QlnOllFJKKeV3/G1Qk+TSlnOllFJKKaV8hLacK6WUUkopv6N9zpVSSimllFJepS3nSimllFLK7+g450oppZRSSimv0pZzpZRSSinld6J1tBallFJKKaWUN2nLuVJKKaWU8jvps91cW86VUkoppZTyGdpyfhOlcxS0O0KKZBb/+1+6/3yw3RFSrNwd+eyOkCJR0VF2R0ixC9cu2x0hxQSxO0KKVNx8zO4IKVY5R3G7I6RIZkcGuyPcNias/8DuCCnyZK2+dkfwe+l1nHP/q8kppZRSymuerd3P7ggp5m8Vc6WSopVzpZRSSinld9Jry7n2OVdKKaWUUspHaMu5UkoppZTyO0bHOVdKKaWUUkp5k7acK6WUUkopv5Ne+5xr5VwppZRSSvkdk04r59qtRSmllFJKKR+hLedKKaWUUsrv6AuhSimllFJKKa/SlnOllFJKKeV30usLodpyrpRSSimllI/QlnOllFJKKeV3tM+5UkoppZRSyqu0cu4FDZvWY/aqn5m35leefrl7vO216lXnl0VT2HJiFS3vb+axbfy0T1i9dzFjv/soreICUK1xDT76cywfLx9H+xcejLe9zdPt+WDxZ4ya/wkDfxhGnsJ5AchTOC/vz/6IEXM/5oNFY2jepZXXMrZs0YRtW5exc8df9O3bK972DBky8N3UL9i54y/+WvEHxYsXASAoKCcLFvzEubO7+eTjd2PLZ8qUkRm/T2brlqVs2riY995902vZAWo0rsnnS8fxxYrxPNjroXjb2z/dgTFLxvLxgjEMnfYeeV37GOCdb4fw3bZpDJw0KNVztWzZhO3bV7Br50r69Xsx3vYMGTLw/ffj2LVzJatWzordrwD9+7/Erp0r2b59BS1aNI5dnyNHdn78cQLbti1n69Zl1KtbC4AhQ/qxccMi1q9byNw5P1CwYP5bz5/Kx4W76b9+w8YNi285Y1wtWjRm69al7NixItHMU6eOZceOFaxYMTNO5h85e3YXH388zOMzDz3UjnXrFrBx42Lef/+tVM/s7t7mjVi7cSEbtizh1T7PJZj/6ymfsmHLEhYt/ZWixQp7bC9SpCDHTm7hpd5PeTVnjDpN7uL7FZOZtvJburz4WLzt1epW4ev5X7L0yEKatG3ksS1foXx89MMopi77hqlLv6FAkVs/ZpOjdpNafLVsIpP++ppHej0cb3vlupX5fO5nzD00m4ZtGnpse+qtnkxY/CUT/xzPC0OfT5O8lRtXZ/iSMYxc9jltXngg3vaWT7XjvUWfMGzeaPp9P5jcbue3rw/8zNC5HzJ07of0nujd83ByvT18NI3aPkbHrmmz/5KrauMafPDnZ3y0fCztEtjPrZ9ux6jFnzJ8/mgG/DAkdj/nLpyXd2d/wPtzP2Lkok9o1qVlWkf3imiMV//YJdUr5yIyV0RypqB8CRHZnto5kvlv/5va39PhcDBwZD+ef/xV2t/zGG0eaEnpciU9yoScOMXAV95lzm8L433+my++Y8BLQ1I7VpLE4eDJd59jVI9h9G3+Mg3a30PhskU8yhzecZCB97/OG/e9yj9z/+bxAT0ACDsdxuAH32BAm9d4u0N/2r/QiVz5cqV6RofDwaefvkf7Dt2pVr0Zjz7SgTvvLOtR5sknHiM8PJyKle5hzGdf8f57VgXl6tVrDB36IW+++V687/vxJ+OpWq0pdeq2pn6Du2jVskmqZ4/J/+x7z/NujyH0vvdFGrZvRJGyRT3KHNxxkL5t+/Baq978PWcV3d96MnbbjPG/8clro72Sa8yn79OuXVeqVmvKY492pEIFz/3a88nOhIedp0LFhnw6ZiLDhw8EoEKFsjz6SAeqVW/G/fd34bMxw3E4rFPKx6OHsXDBUqpUaUytWi3YtXsfAB99NI6atVpQ+66WzJ27mLcHvnbL+b1xXAB06HAf/166dEv5ksrcoUMPqle/l0ceaR8v8xNPPEp4+HkqVWrEZ599xXvvDXDL/BFvvvm+R/mgoJyMGPEWrVt3pmbN5uTPn4emTe9O9ewx+T8YPYSHH3yKerXvo9PD91P+zjIeZbr1eJjz4eepVe1exo2dxJB3+3tsf3/UQBYvWuGVfAnl7fN+b/p2HUC3pj1p3rEZJcoW9yhz6sRphr/2PxbPWBLv829/+gbTxv1MtyY9ebZtL8LOhqdJ5hffe5G3u7/DM82eo2mHJhQrW8yjzJkTp/moz0csnbHUY33FWhWoVLsiz7fsxXPNX6BctXJUrVfFq3nF4aDbsGf4+In3GdjiVeq2b0ihMp7XkKM7DzGsXX8Gte7D+nlreGRAt9ht169eZ3Cbvgxu05cxz4z0atbk6timBV+OTvjcYBdxOOjx7jP8r8d79G/+CvXa30OheNfqQ7xzfz/euq8Pa+eupvMAq4Ew/HQYQx8cwMA2rzO4w5u0e+FBcnrhWq1SR6pXzo0xbYwx3j97+agqNSty7NBxjh8JJiIikrkzFtH0Ps+WmOBjIezduR8THR3v8//8tZ5L/15Oq7gAlKlelpOHQzh97BRREZGsnrWS2i3qepTZuXo7169eB2D/pj0EFcwNQFREJJHXIwEIzBCIOMQrGe+6qzoHDhzm0KGjRERE8PMvf9Cuneedf7t2LZn63a8A/PbbnNjKyeXLV/j773VcvXbNo/yVK1dZvnw1ABEREWzetI3CRQp6JX/Z6mUJORzCqaOniIyIZOWsFdRp6bmPt6/exvWrVsa9m/aQ27WPAbat2sqVf6+keq46d9Xw2K8//TyTdu08n360a9eSqVN/AWD69Dk0a9rQtb4VP/08k+vXr3P48DEOHDhMnbtqkC1bVho2rMs3k6YB1r49f/4CABcv3rgfzpwl8y33F/TGcQGQJUtmXnnlGUaMGHNL+ZKT+ZdfZiWY+bvYzHPjZb527apH+ZIli7Fv3yHOng0F4M8/V9KxY+tUzw5Qq3Y1Dh48wpHDx4iIiOC3X+fQpm1zjzKt2zZn2ve/AzDz9/k0blI/dlub+5tz5NAxdu/a55V8cVWocScnDp8g5GgIkRGRLJm5lIatGniUOXn8FAd2HcREex6PJcoWxxngZP1fGwC4cvkq167GP15SW/nq5Qg+HMzJoyeJjIhk2R/Lqd+ynkeZU8dPc2j3YaLj/A4ZY8hwRwYCMgQQmCGQgECn128oSlUvw+kjJznjuoasnbWSGi3v8iiz2+0acmDTXnIVyJ3Qt/IZtatXIUf2bHbH8FC6ehlOHQ6J3c9rZq2kVos6HmV2eVyr9yZyrQ7w2rU6rRkv/2eXFFfORaS/iPR2ff2xiPzp+vpeEflORA6LSB5Xi/guEZkoIjtEZKGIZHKVrSUiW0RkNfCi2/euJCJrRWSziGwVkbKu77NbRKa41v0qIpndvs9yEdkgIgtEpKBrfWkRme9a/5eI3OlaX1JEVovIOhFJ+Dn2LcpfIB8hwadil08FnyZ/gbxJfMJ+uQoEcS7kbOzyuZBz5CoQlGj5Jo82Z8uyjbHLQQXzMGr+J3y+5iv++PI3wk6HpXrGQoUKcOx4cOzyiRMhFC5UIF6Z464yUVFRXLhwkdy5k9cykCNHdtq2bc7SpatSL7SboAK5ORvsuY9z50/84tT80RZsXLrBK1ncFSp8Y59BIvu18I19HxUVxfnzF8idOxeFC8X/bKHCBShVqjhnz57j668+Zt3aBYz/8gMyZ84UW27YsDc4eGAdnTs/wJChH9xafi8dF0MG9+OTTyZy5Urq3xAVSmi/FcqfaJnkZD5w4AjlypWmePEiOJ1O2rVrSZEihVI9O0DBQvk5cTwkdjn4xEkKxst/o0xUVBQXzv9LUO5cZM6ciVdee45RIz7zSraE5C2Qh9PBZ2KXz4ScIU+BPMn6bNFSRfj3wiXemziErxd8Sa+3n419OuRNuQvk4Yxb5rMhZ8mTzMrsro272bJ6K9PWf8+0Dd+zYflGju0/5q2oAOTKH0So2/ktNCSUXEmc3xo9ci/b3K4hgXdkYNAfo3j79xHUaFkn0c/d7nIVyE1oyLnY5dCbXKsbP3pvnGt1bobPH82nayYy+8vfCffCtVqljv9yllkB3OP6ujaQVUQCgYbAX3HKlgXGGmMqAeFAJ9f6SUBvY0z9OOWfBz41xlR3fe/jrvXlgQnGmKrABaCX69/8DHjIGFML+AaIedY7AXjZtb4v8IVr/afAOGPMXcDJxH5AEXlWRNaLyPqwK6dvvkc8Phx/lZ13X8khCYdOUMMHGlOqShlmjf89dl1oyFneuO9VXmv0PI06NSVHnhypn1HiZ4zb6ppAkWS1zDqdTqZ++zljx07i0KGj/zljUpKTP0bjB5pQumoZZoz/zStZ3CVvvyZUJvHPBjid1KhRhfHjv+WuOq24dOky/fu/FFtm0KBRlCp9F9Om/U6vXk/G+x6pnz/+55I6LqpWrUjp0sX544/5t5QtMf99nyeeOTz8PL17D2Tq1LEsWfIrR44cJzIy8tbDJiBZ2RIp8+bAVxg3dhKXLqXh08GEGgiT+cTGGeCkap3KjH13PM+26UXBYgVp/Yj33quJkfAxm7zPFipRkKJlitKlTjcev6sr1RpUo3LdyqkbMK4UHK/1OzaiRNXSzJswM3Zd3wbPMaz9G4zv/QmPD3qSvMXSpl+/v0mwrTuR4+LuBxpRqkoZ5oyfEbsuNOQcb93Xh9cb9eKeTk3J7oVrdVqLNsarf+zyXyrnG4BaIpINuAasxqpI30P8yvkhY8xmt8+VEJEcQE5jzHLX+qlu5VcDb4nIG0BxY0xMs9UxY0xMk+Z3WDcC5YHKwCIR2Qy8DRQRkaxAA+AX1/rxQExfhbuBaQn8ux6MMROMMbWNMbVzZcqXjF1yw6mQ0x6tSPkL5eP0ybNJfMJ+oSfPkbvgjZak3AVzE3YqNF65yndXpeNLD/Hh08NjH4+5CzsdxvG9xyhfp2KqZzxxIoSibi2BhQsXJDjkVJwyJ2NbC51OJ9mzZyM09OaPc7/4YhT79x/is8+/Tt3Qbs6FnCVPIc99HHo6/j6u2rAaD730CCOeei/BfZzaThwP8WhhTXC/Hr+x751OJzlyZCc0NIzjJ+J/NiT4FMdPhHD8eAhr120CYPpvc6hRPX6f1x9//J0HHmhza/m9cFzUq1uLGjWqsmfP3/y55DfKli3JwoU/31LOuJnj7beQ04mWSe6xPHfuYho16kCTJg+wb99B9u8/nGqZ3QWfOOnR/atQ4QKcjJPfvYzT6SR7jqyEhYZT+65qDH23P1t2LOOFXk/Qp+8LPPNcN7zpTMhZ8hW68fQyb8G8nD11LolP3HA65Az7tu8n5GgIUVHRrFywinJVyt78g7fobMhZ8rplzlMwD+eSmblBqwbs3rSbq5evcvXyVdYvXU+FGnd6KyoAYSfPEeR2fgsqGER4Aue3indX5f6XOvHp0yM8zm8xLbhnjp1i95odFK9UMt5nlXWtDnLr7hiUyLW60t1Vaf/SQ4yOs59jhJ8O44SXrtUqdaS4cm6MiQAOA08Cf2NVyJsCpYFdcYq7d86LwhpXXUjkXs8Y8wPQHrgCLBCRmKFM4pY3ru+zwxhT3fWnijGmpetnCndbX90YUyHOZ71m+6ZdFCtVlMLFChIYGECbji1YuiBtXnz6rw5s2UeBkgXJWzQfzsAA6rdryIZFaz3KlKhUkqdH9OLDp4Zz4dz52PVBBXITeEcGALJkz0L52ncSciCY1LZ+/RbKlClBiRJFCQwM5JGH2zN79iKPMrNnL6JbV2sUlAcfbMuyZTfvojJkSD9yZM/G632HpHpmd/u27KNgyULkK5qfgMAAGrZrxLo4+7hkpVK8MOJFhj/1Lufd9rE3rVu/mTJlSsbu10cf6cDs2Z4vKs+evZBu3azRIjp1astS136dPXshjz7SgQwZMlCiRFHKlCnJ2nWbOHXqDMePB1OuXGkAmjVryK5dewEoU+bGRbfd/S3Zs+fALeX3xnExYeJUSpaqTfnyDWh274Ps23eIli0fuaWc8TPf2OcPP9wuwcxdYzO3Ydmyv2/6ffPmtS7aOXPm4NlnuzFp0rSbfOK/2bhhK6VLF6dY8SIEBgby4ENtmTfX80XK+XOX0LmLNZJEhwfuY8XyNQC0admZapWaUK1SE8Z9MZnRH45j4vhE20lSxe7NuylSsjAFixYgIDCAezs0ZeXCm+9P67N7yJYzGzmDrBbGmnfX4PDeI96MC8CeLXspXKIQ+V3niybtG7Nm0ZpkffZM8Bmq1q2Cw+nAGeCkSr0qHPVyt5ZDW/aTr0RB8hSxriF12jVk06L1HmWKVSpJj+HPMebpkVw8dyF2febsWQjIYE25kjVXNsrWupPgfcdR8R3cst/jWl2vXUM2LlrnUaZ4pZL0HPE8o58akei1OnP2LJStfSchB06kaX5vSK99zv/rJEQrsLqL9AS2AaOBDcYYk9AjT3fGmHAROS8iDY0xK4EuMdtEpBRw0BgzxvV1VeAgUExE6htjVgOdgZXAHiBvzHpXN5dyxpgdInJIRB42xvwiVqCqxpgtwCrgMazW9y54QVRUFO8P+JAJP47B4XTw+7RZHNhziJf6P8uOLbtYuuAvKlevwKeT/kf2nNlo0vIeXuz3DB0adwbg25njKVmmOJmzZGLJplkMeu09Vi37xxtRY0VHRTN50EQGfDsYh9PJsp8Xc3zfMR7q05lDW/ezYfE6Hn/rCTJmzsgrX1ijLpwLPsOHTw+ncJkidH37SYwxiAizJ8zk2J7Uv3hFRUXx6qvvMHvWdzidTiZP+Yldu/YyaNDrbNywldlzFjFp8o9M+uYTdu74i9DQcLp1vzEs4J49f5M9WzYyZAikXbtWtL2/CxcvXmTAm73ZvXsf/6yZB8C4LyczadKPqZ4/Oiqaie98yeCpQ3E4HSz5aTHH9h6lc58u7N+2j3WL1tJj4JNkzJyRfuOsocTOBJ9hxFPWaAHv/zqSwqWLkDFLRib+M4mx/cawecWmW84VFRXFK6++zZw5P+B0OJg85Sd27tzL4MF92bBhC7NnL+KbST8yefIYdu1cSVhYOF26WkP/7dy5l19+ncXWLUuJjIqi9ysDiXa95Pzqa+/w7ZTPyJAhkIOHjvL0032sn+P9AZQrVxoTHc2Royd48cVbGzbNG8fF7t3efVExJvOsWVNxOp1Mic3chw0btjFnziImT/6Jb775hB07VhAaGk737je6Be3Zs4psbpnvv78ru3fv46OPhlClitUSNnz4J+zff8hr+fu/PpTpMybhdDr5fuov7N61jwFvv8LmjduZN3cJU6f8zJdffcSGLUsICwvnqSde9UqW5OWN5uO3P+OjH0bhcDiY89M8Du89wlN9n2D3lj2sWrSaO6uV5/2vh5ItR1YatKhPz9d70L3ZU0RHRzN22Hg++elDENi7bR+zfpjj9czRUdGMfWccw797D4fTycKfFnJk71G6v96NvVv3smbRP5SrVo5BE98hW46s1Gtel+59uvJs8+f5a85KqjWoxvhF4zAG1i9fzz+LvX8N+X7QV7z+7Ts4nA7++vlPgvcdo+Nrj3F42342L17PIwO6c0fmjPT64nUAzp04y5hnRlKoTBF6DH+OaGNwiDBn3O8E77e/ct5v8EjWbdpKePgF7u3YlV5PdaNTO+93aUpKdFQ0UwZ9Rf9vB+FwOlj+8xJO7DtGpz6PcWjrATYuXkfnt7qTMXNGen/RF4BzwWcZ/fQICpUpwuNv93B1SYS5E2ZyfI93unGqWyf/ZbQEEbkXmI/VPeWSiOwFvjTGjBaRw7j6ogOzjTGVXZ/pC2Q1xgwRkZg+4peBBVj9xiuLyACgKxCB1Sf8cSA7MBfrhqABsA/oZoy5LCLVgTFADqwbjU+MMRNFpCQwDqs7SyDwozFmmGv9D66y04G3jTFZk/pZK+Wv69sdxuOomtE7o41402+nvP/iY2prk6+63RFSZPbJjTcv5GPS4sW71Jbg+xs+LFNABrsjpFjlHMVvXsiHZHb43z4u5Mxsd4QUm7D+1l4ut8OTtfraHSHFvjvym0+d5Crkq+PVOtqu02tt+Xn/U8u5MWYJVqU3Zrmc29clXF+exeoTHrP+Q7evNwDV3L7lENf6EcAI939LRLID0caYeDMBuPqzN0pg/SHgvkTWu7+E6hsDqiqllFJKKcV/79ailFJKKaWUbXx9NLz/yucr58aYw7i1wCullFJKKZVe+XzlXCmllFJKqbjsHIvcm/zvjSullFJKKaXSKW05V0oppZRSfkf7nCullFJKKeUjtFuLUkoppZRSyqu05VwppZRSSvmd9NqtRVvOlVJKKaWU8hHacq6UUkoppfyOMdF2R/AKbTlXSimllFLKR2jLuVJKKaWU8jvR2udcKaWUUkop5U3acn4TK+pktDtCiszbkNXuCCk2smJ5uyOk2Eehme2OkCL5suS0O8JtIcrP+j92z1nd7ggp9kaVYLsjpIg4Iu2OkGL1/j5jd4TbwqQNH9odwe+ZdDrOuVbOlVJKKeXXnqzV1+4IKaIVc5UUrZwrpZRSSim/o33OlVJKKaWUUl6lLedKKaWUUsrvpNc+59pyrpRSSimllI/QlnOllFJKRD/NPQAAIABJREFUKeV3orXlXCmllFJKKeVN2nKulFJKKaX8jtHRWpRSSimllFLepC3nSimllFLK7+hoLUoppZRSSimv0pZzpZRSSinld9LrDKFaOVdKKaWUUn5Hu7UopZRSSimlvEpbzr0gsGYdsjzzMjgcXF00h6u//hCvTIaGTcnU+QnAEHXoAP9++C6OvPnJ9ta74HBAQABXZ/3Gtfl/pEnmgk2qcte73RCHg/3TlrHj81kJlivW9i4aTXyFufe9Q+jWQ2TIlZVGE3qTu3opDv68gnUDv02TvBkb3EVQ317gdPDv7/O4MPlHj+1Z2rUk16vPEnX6LAAXf5rJvzPmcUftagS9/kJsucASxTgz4D2uLPvb65krNK7Gg4OewOF0sPqnP1k8bqbH9qZPtaX+Y82Iiozi39AL/ND/S8JOWPnbv9mFSs1qIA4He/7ayvShk72Ws8m9DRk24k0cTifTpk5n7CdfeWzPkCGQT8eNoEr1SoSFhvNCz9c5fizY+hkrlWPU6MFkzZaVaBNN22aPcu3adTp0asPLfZ7BGMOpkDO8/NwbhIWG+2TegMAAfp87NfbzBQvl57efZzP4rZGpkheg6b0NeXfkWzidDr7/9lc+TyDzZ1+Oomr1ioSFhvNczz4cO3oj8wcfDyVbtqxER0dzX7OHuXbtOm++/QoPP9aBnDmzU7pI7VTLejPlGlejw6DuiNPB2p+Wsmyc5zmrXpfm1O/WAhMdzbVLV5k+4CtO7z+RZvnAP8/JgTXqkNmV+dqiOVydnkDmu63MxliZL422Mmd980bma3PSLrO7e5rVZ+D7fXE6Hfzy3QwmjJnisb12/RoMfO91ylcsw2vPDmTBrCVpnrFq4xp0G9wTh9PBsh8XM2vc7x7bWz/djiaPNScqMoqLoReY0G8s506cIXfhvLw6vj8OhwNnoJOFk+fy5/cL0zx/XG8PH82KVWsJypWTGd99aXecNJFeJyG6LSvnIlICaGCMiX+2u1UOB1mef5UL77xO9Lkz5Bg9noh/VhF17MiNIgULk+mhLlzo/yLm0r9IjpwARIed43y/FyEyAjJmIufnk7i+dhUm9Fyqx3QnDqHO8B4seWwkl0NCaT13GMcXbOD8vmCPcgFZMlL+qVac2bA/dl3U1Qi2fPArOcsXIeedRbyaM5bDQdAbL3O61xtEnjpDwe/GcmX530QcOupR7NLCZYSN+txj3bX1Wwjp/Lz1bbJno9DMKVxds8HrkcUhPDysJ2O7vk/4yXP0/WME2xet56RbJeX4zsN80G4AEVev07BrCzoM6MLklz6lZM1ylKpdnpH39QPg1V+HUaZeRfav2ZnqOR0OB+9/MJDODzxDSPAp5v75EwvnLWXfngOxZTp368T58xdoWKs17R9szcAhfXjhqb44nU7GjB/JK88PYOf2PeTKlYOIiEicTifDRrxJk3rtCQsNZ+DQ13nymccZPeoLn8x77dp1WjbqFPv5eUt/Zu7sRbec1T3ziA/f4ZGOTxESfIr5S39m4byl7HXL/Hi3hwgPP0/9mvfR4cE2vD2kL8/17IPT6WTshP/x0nNvuDLnJCIiEoCF85fxzcQfWL1hXqplvRlxCA8Me5KJXYdz/uQ5Xv7jfXYu2uBR+d40cxVrvl8MQMXmtWj3Tje+7pF6Nzo35YfnZBwOMj/3KhcHW5mzfzie62tXER0nc8aHunDhjfiZL7xxI3OOMWmU2SO+g8Ej3+DJh1/kZPAppi/8liXzV3Bg76HYMiHHT/Lmy0N4qle3NMvlThwOerz7DCO7DCX05DmG/fE/NixeR/C+47FlDu84xDv39+P61evc27UVnQd05/OXPiL8dBhDHxxA5PVI7sickZELP2HjonWEnw6z5WeJ0bFNCx7v1J633v3Q1hzq1t2u3VpKAI974xsHlK1AVMgJok+FQGQk11b8SWDdhh5lMrZqx9W5v2Mu/QuAOe9qQYyMtE6ogAQGWi0faSB3jdJcPHyKf4+eIToiisMz11CkVa145ar1f4idX8wm+lpE7LqoK9c4s3YvUW7rvC1D5fJEHg8m8oS1jy8tWEamJnen+Ptkbt6Iq6vWYa5e80JKT8Wrl+HMkVOcO3aaqIgoNs76myot7/Ios2/1DiKuXgfg8KZ95CyQG7AmWQi8I5CAwAACMgTiDHBy8cx5r+SsUasKhw8e4+iR40RERDDzt7m0atPUo0zL1s34ZZrV6j9n5kIaNq4HQONmDdi1Yy87t+8BICzsPNHR0YgIIkLmLJkAyJYtC6dOnvHZvO5KlipGnrxB/PN36t3A1ahVlUMHj8ZmnjF9Lq3aNPMo06pNM352ZZ49c0Fs5ibN7mbn9j1umcNjM29cv4XTp1JnvyZX0eplOHvkJKGu43rLrNVUaunZan/t3yuxX2fIfEea9xH1x3NyQNkKRJ+8kfn6X3+SoY5n5jtatuOaD2V2V7VmJY4cPsaxIyeIiIhkzoyFNG/d2KPMiWMh7Nm5n2gTnch38a7S1ctw6nAIZ46dIioikjWzVlKrRR2PMrtWb+e665y8f9Neggpa5+SoiEgir1s3xYEZAhCHpG34RNSuXoUc2bPZHSNNGWO8+scu6arlXES6A30BA2wFooALQG2gANDfGPMrMBKoICKbgSnGmI9TK4Mjdx6iz56OXY4+d4bAchU8yjgLWy3M2Ud9Dg4HV6ZNJmLjWuvzefKSbdAonIUKc+mbcWnS2pG5QC4uB4fGLl8OCSVPzdIeZXJVLk6WQkGcWLyZis+39XqmpATkzUPkyRv7OOr0GTJUvjNeuczN7iFjzapEHDlO2EfjiIpTccnSqgkXvvvV63kBcuYPIjz4xv/L8JBzFK9eJtHy9R5pys5lmwE4vHEfe1fv4N114xGEFd/O59QB73QLKFAwP8EnQmKXQ4JPUaNWVc8yhfIRfOIkAFFRUVy4cJFcQTkpVboEGMP3v04gd55czPxtHuPGfENkZCQDXn+XJStncPnyFQ4dPMJbfd/z2bzuOnRqyx+/zU+VrDEKFryRJyZzzTiZC7r9XFFRUVy8cJGgoJyUKlMCA0ybPpHceYKYOX0uY8d8nar5UiJH/lycdzuuz4eco2gCx3X9bi1o9HRbnIEBTHg8df7fJ5c/npMldx6i4mQOiJu5kJU528jPkZjMm25kzvrOKJwFC3N5ctpkdpe/YD5OnjgVu3wy+DTValVO0ww3k6tAbkJDbuyX0JBzlK5RNtHyjR+9ly3LNsYuBxXMTd9JA8lfoiDThk+xvdVcpS/ppuVcRCoBA4FmxphqwCuuTQWBhsD9WJVygDeBv4wx1ROqmIvIsyKyXkTWTzkSEnfzzYLEWxXv5svpxFmoCBfeeoV/PxxGlpf7IVmyAhB99gzne/ck7NnHyXjvfUjOXCn79/+Lm2UWofaQrmwYmvq9gP6TBPLGHU3pyoo1nLi/KyGPPsvVfzaSZ1h/j+3OPEEElinJldXrvRjUTXKOC5faHRtSrGpp/pxg9RPNUzw/BcoUZlC9F3in3vOUa1CZ0nUqJPzh1I8Zr/VASLAQzgAnd9WryUvP9qdj6260bnsvDRvVJSAggO49H6VV44eoWaEJu3bs5eXXnvHZvO46PNiaGdPnpkrW2DwJHQtxDuAEyxgIcDqpW68mLz7Tjw73daH1/c1p2KhequZLkWT8LgKsnrqIUY1fZe7IH2j28gPez+XOH8/JCR6zcZZdmS8OdGV+yTPzhVd6Ev7849zR9D4kR1pkviE5v5d2S7CtO5GIdz/QiFJVyjBn/IzYdaEh53jrvj683qgX93RqSvY8ObySUyUtGuPVP3ZJN5VzoBnwqzHmLIAxJqYpeIYxJtoYsxPIn5xvZIyZYIypbYyp3aN4wRSFiD57BkeefLHLjtx5iQ49G6/M9X9WQlQU0adOEn3iGI5Cnv21Teg5Io8eJrCiZ4uaN1wOCSVzoaDY5cwFg7hy8kYrQGDWjOS4swgtpg+k4z8fk6dmaZpM7kNQ1ZJez5aQyNNnCChwYx878+Ul6oxny1D0+QsQYT3a/ff3uWS4s5zH9swtGnN56SqIjPJ+YCD85DlyFsodu5yzYG4uJNDSUu7uKrR86UEmPP2/2MemVVvV4fCmfVy/fI3rl6+xa9lmSiTRwnMrQoJPUajwjWO+YKH8nHJ7SnGjTAEAnE4n2bNnIyzsPCHBp1izaj1hoeFcvXKVPxf9ReVqFalUxXqqceTwMQBmzZhPrbrVfTZvjIqVyxMQ4GTbltTt2x/slicm88mQ03HKnIz9uZxOJ9myZyMsLJzg4FOsXrWO0NBwrly5ypJFK6jqljmtnT8ZSg634zpHIsd1jC2zVlOpRdq9rAr+eU42587gvFnmc26ZT58k6sQxHAXjZ446dpiASt7P7O5k8GkKFL5xuS1QKB+nU6krW2oJPXkutpsKWC3hYadC45WrdHdV2r/0EKOfHhF7TnYXfjqME3uPUb6Ofb+HKv1JT5VzIeH73mtxynhV5L7dOAsVwZG/AAQEcEejZkSsXeVR5vqalQRUqWEFyp4DR6GiRJ8MxpE7L2TIYK3PkpXACpWJOnHM25E5t/kg2UoWIEvRvDgCnZToUI/jC288vou4eIVfK7/AjLqvMaPua5zdeIBlT4wmdOuhJL6r91zfsYeAooUJKGTt4yytmnBluedoK848N242MjWuT8Rhz5dFs9zXjEvz/0yTvABHtxwgb4kCBBXJizPQSc12Ddi2yLPVvkilEjw2/GkmPv0//j13IXZ9WPBZytStiMPpwBHgpHTdCpzafzzuP5EqNm/cTsnSxSharDCBgYF0eLANC+ct9SizcP5SHu7cAYC2HVqyasU/ACxfsooKlcqRMVNGnE4n9e6uzb49BzgZcoqy5UsTlNtqvWvUpAH79xz02bwxOnRqk+qt5lbmbZQqXZxixa3MHTslkHneUh5xZb6/QytWrVgDwLIlK6lQqTyZXJnr332Xx4ukae34lgPkKVGAXK7julq7+uxc5Nk/P0+JGzcidzarwbnDJ+N+G6/yx3Ny5L7dOAoWwZHPypzhnviZI9asJDAmc7YcOAoXJfpUMBInc8CdlYlOg8zutm3aSYmSRSlSrBCBgQG07diSJfNXpGmGmzm4ZT8FShYkb9F8OAMDqNeuIRsXrfMoU7xSSXqOeJ7RT43gwrkb7/kEFchN4B3WPs6cPQtla99JiJe6GqqkaZ9z37cE+F1EPjbGnBORoCTKXgS889ZEdBSXvvyE7EM/tIbAWjyXqKOHydSlJ5H7dhOx9m8iNq4lsMZd5Bg7BaKjuTxpHObiBQKq1yZbz15Y9xjCld9/IupI6lRikmKiolk3cAr3/tAfcTo48ONyzu89QdV+nQjdcsijop6Qjv98TGDWTDgyBFCkVW3+7Dwy3kgvqSoqmtBRn5Fv7EhwOPj3j/lEHDxCjud7cH3nXq6sWE22xx4gU+P6VqvS+YucHfy/2I87C+bHmT8v1zZs9V7GOKKjovl10Df0+vYtHE4Ha35exsl9x2nz2sMc3XaQ7Ys30GFAVzJkzsiTX7wGQNiJs0x85gM2z11DuQaVeXPBh2AMu5ZvZvuSpP+f/FdRUVG83f99fpg+AYfTwU/f/87e3QfoO+AltmzewaJ5S/lx6nTGfDmSlRvmER52nl5P9QXg/PkLTPhiCnOX/ITB8Oeiv1iy0Logf/y/L/htzhQiIiM5cSyE13q95dN5Adp1bEW3R15I7J++pcxv9XuPadO/wul0MO2739izez/933qZzZu2s3DeUn6Y+iufjx/F6o3zCQ87z3M9X4/NPH7sZOb/+QvGGJYsWsHihcsBeGdoXx54qC2ZMmdi4w7re3w4cmyq53cXHRXNzEGTefrbATicDtb9vIxT+47T8rWHOL7tEDsXb6BBj5aUubsK0ZGRXDl/iZ9eH+fVTAmE9LtzMtFRXJ7wCdmGuDIvmUvUscNkerwnkftdmTe5Mn8+BRMVzZXJrszVapO5Zy+r744IV2ekUWY3UVFRDBvwAV///BlOh5Nfp/3B/j0H6f3Gc2zfvIs/F6ygSvWKjJ3yAdlzZKdpy3vo3f9Z2t7zaJpljI6KZsqgr+j/7SAcTgfLf17CiX3H6NTnMQ5tPcDGxevo/FZ3MmbOSO8vrHPGueCzjH56BIXKFOHxt3vE7GLmTpjJ8T1Hb/Ivel+/wSNZt2kr4eEXuLdjV3o91Y1O7VrZHUv9B+Jr/cBuhYj0APphvQi6ybV6tuslUETkX2NMVhEJBOYDeYDJSb0Qeq5dY7/aQfM2FLU7QordUyBtW9JSw0ehuW9eyIdMP7/d7gi3hSibRp74r7rnTJ3uRWnpjSpevPH3AvHD59P1/r5kd4QUq5O5mN0RUmTSBv8c7jAwTynfGJrGJWvmkl6to/17+ZAtP296ajnHGDMFmJLE9qyuvyOAe9Mql1JKKaWUUsmRrirnSimllFLq9hB3pKv0wg8fuCmllFJKKZU+acu5UkoppZTyO9Hp6L1Jd9pyrpRSSimllI/QlnOllFJKKeV30tOIg+605VwppZRSSikfoS3nSimllFLK7+hoLUoppZRSSimv0pZzpZRSSinld9Jrn3OtnCullFJKKb+TXivn2q1FKaWUUkopH6Et50oppZRSyu+kz3ZzbTlXSimllFLKZ0h67a/jD0TkWWPMBLtzJJe/5QX/y+xveUEzpwV/ywuaOS34W17QzGnB3/Kq+LTl3F7P2h0ghfwtL/hfZn/LC5o5LfhbXtDMacHf8oJmTgv+llfFoZVzpZRSSimlfIRWzpVSSimllPIRWjm3l7/1CfO3vOB/mf0tL2jmtOBveUEzpwV/ywuaOS34W14Vh74QqpRSSimllI/QlnOllFJKKaV8hFbOlVJKKaWU8hFaOVdKKaWUUspHaOU8jYnIK8lZp1JORBwist3uHLcDEQmyO0NK+FteABH5UEQq2Z0juURkVHLW+RoRKSAi7UWknYgUsDtPcohITRHpLSIvi0hNu/Mo+2ndIn3RF0LTmIhsNMbUjLNukzGmhl2ZbkZECgPFgYCYdcaYFfYlSpyIfA8MMMYctTtLSohIA6AEnvv4W9sC3YSI7AM2A5OAecbHTyT+lhdARJ4GnsQ6JiYB04wx5+1NlbhEzm1bjTFV7cp0M659PAj4ExCgMTDMGPONrcGSICKDgIeB31yrOgK/GGPesy9V0kQkJ9Cd+Oe43nZlSoiIXAQSPTcYY7KnYZwU8ce6hUqcVs7TiIh0Bh4HGgJ/uW3KBkQZY5rbEuwmXC1fjwI7gSjXamOMaW9fqsSJyJ/AXcBa4FLMel/NCyAiU4HSWJVH933sUxcudyIiQHOgJ1AH+AmYbIzZa2uwRPhbXnciUh6rkt4ZWAVMNMYstTfVDSLyAtALKAUccNuUDVhljOlqS7BkEJE9QANjzDnXcm7gb2NMeXuTJU5EdgE1jDFXXcuZgI3GmAr2JkuciPwNrAG2AdEx640xU2wLlQQRGQacBKZi3bR1AbIZY/5na7AE+GvdQiVNK+dpRESKAyWBEcCbbpsuAluNMZG2BLsJ18WrqjHmmt1ZkkNEXgaOA6Hu640xy+1JdHOui21Ff2jNTYiINAW+A7IAW4A3jTGr7U2VOH/KKyJO4H6synlR4Gesi/AlY8xjdmaLISI5gFwkcG4zxoQm/CnfICJLgNbGmOuu5QzAXF+u0IjIPKCzMSbctZwT+M4Yc7+9yRKXUKuuLxORf4wxdW+2zhf4a91CJS3g5kVUajDGHAGOAPXtzpJCB4FAwC8q50B+4BVgI/ANsMAPKr3bgQJAiN1BksvVwtgV6AacAl4G/gCqA79gXSx8hr/lBRCR0UB7YAkw3Biz1rVplOum2VcYY8xhEXkx7gYRCfLxCvoJ4B8RmYnVnaEDsFZE+gAYY0bbGS4R14AdIrIIK3MLYKWIjAHf6yriMlVEngFm43Yt8eFjI0pEugA/Yu3jztx4qulT/LhuoZKgLedpTEQeBEYB+bAelwnWxc0n+7KJyHSgGlYFwf2k6osXACC2C0NLrNbG2litjV8bYw4k+cE0JiKzsE782bAqiWvx3Me+3BVnL9Yj30nGmONxtr1hjPGpFwH9LS+AiPQEfjTGXE5gWw5f6X8uIrONMfeLyCGs41ncNhtjTCmbot2UiAxOarsxZmhaZUkuEemR1HZf7CriunF7HwjnRp9unz02RKQE8ClwN1beVcCrxpjD9qVKmr/VLVTStHKexkRkP9DOGLPL7izJkdiFwBcvAO5EpBpW5fw+YClQD1hkjOlvazA3ItI4qe0+3hVH/OCJRCx/yxtDRHIBZYGMMet89WVspRIjIgeAusaYs3ZnSa/8rW6hkqbdWtLeKX/65THGTHH1wyznWrXHGBNhZ6akiEhvoAdwFvgK6GeMiRARB7AP8JnKeUzlW0RGGWPecN/mehHXZyvnQB4R6Q9UwrPi2My+SEnyt7wxI4m8AhTBelm4HrAa8MnMInI3sNkYc0lEugI1gU98eeQkEakNDCT+aFS+PMLM/cC73MjsDy2kO4B4T4B8lYiUA8YB+Y0xlUWkKtDel0fEwc/qFipp2nKexkTkU6z+xTPw7MLwW6IfspGINAGmAIexLgJFgR6+2nrnesv+a1c/vLjbKvjiyctPh6BbiDXiSV/geawbojNxbzJ8hb/lBRCRbVgjD60xxlQXkTuBocaYR22OliAR2YrVBa4qVheir4EHjTFJPiGyk6vvfj/ijyIS7/zhK1wtpA8C2/zlaZCI/I51Y7wUP+geKSLLsY6L8TFDEYrIdmNMZXuTJc7f6hYqadpynvayY7UgtHRbZ7gxZq2v+QhoaYzZA7EtCtOAWramSoQxZlAS23yqYu4+BJ2rYhMjG/C3PamSLbcx5msRecX1BGC564Lmq/wtL8BVY8xVEUFE7jDG7HYNq+irIo0xRkQ6AJ+69neS/aN9wBljzB92h0ihY8B2f6mYu8xw/fEXmY0xa63Xl2L5+qgn/la3UEnQynkaM8Y8aXeGFAqMqZgDGGP2ikignYHSkR+AefjhEHRATNemEBFpCwRjdb/wVf6WF+C4a5i8GcAiEQnDyu2rLorIAKxRcRq5hoH09XPFYBH5ivgvvPtyhaY/MNd1c+me2RdHlgF8/x2lBJwVkdK4Xl4VkYfw/dG0HMArbkNs5sJqXFN+SLu1pDERyQg8Rfy+rz1tC5UEEfkG6wQ11bWqK+D0w5sMn+aqyOTHs9+rL/fVvR9rwouiwGdYrTZDfbUV0t/yxuV6eTgHMD9mTG5fIyIFsCZDWWeM+UtEigFNjG/PdPsdcCdWn+iYbi3GV8/HENtF61/id8XxuZFlYriN5OPBh0drKQVMABoAYcAhoKuPj9YSbzbQhNYp/6CV8zQmIr8Au7EuYsOwZh7bZYx5xdZgiRCRO4AXsSY+EWAF8IXxk0mJ/IGIvAQMwRp/272C4LN9zpX3iEhQUtt98amK6+ZygfHhyXsSIiLbjDFV7M6REiKy3hhT2+4cKeGaZyBGRuBhICipboi+QESyAA5jzEW7s9yMiGzBuhkOcy0HAcv97fhWFq2cp7GYO9mYF/5cXUQW+PKoETFcv+xFjDFbb1pYJZvrBa+6xjWFuC8Tkc9IoAUshq+94OVvecGjlVGAYlgtdwLkBI4aY3xuwiQAEfkD6OYr468nh4hMBD42xuy0O0tyichI4E9jzEK7s9wKEVlpjGlod46EiEh+YDhQyBjTWkQqAvWNMV/bHC1RItIdGAD8inX+eAR43xgzNckPKp+kfc7TXkzf13ARqQycBErYFydpIrIMa5bCAKzh3M6IyHJjTB9bg6UvxwB/qdCsd/19N1ARawQUsFrCNtiSKGn+lpeYyreIfAn8YYyZ61puDfhyy/RVYJtYM1deilnpizdAbhoCPVw3RNe4MSyhLz+1ehHoLyLXgev4wVCKIuI+GpUDa3K4bDbFSY7JwCSsYTYB9mKdO3y2cm6M+VZE1mMNtSpYIyX5zU2n8qQt52nMNXbxdKAK1gkgK/COMWa8nbkS49bS/zRQ1Bgz2NeH+fM3IvI1UB6Yg5+84CUiS7FG8YlwLQcCC40xTe1NljB/ywsgIhuMMbXirPPZLg2Jjcziyy8DikjxhNb78lCK/sj1+xdT2YjEGpr3Q2PMXttCJUFE1hlj7nLvsy0im40x1e3Opm4P2nKe9pa4+oStAEoBiIhPPqZ2CRCRgliPyAberLD6T466/gTi+6NbxCiE1fIV0/85q2udr/K3vGCNGPE28B1WxaYr4LNdn4w1YVkmoJj7CE++zBhzREQaAmWNMZNEJC/WseGzxBrfrwtQ0hjzrogUBQoaY9baHC0prYFOWE+JY+odj2G9d+WLLrn6yceM1lIP/3m6qdIBrZynvelYM+e5+xUfHTcc6+S5AFhpjFnneot9n82Z0pu5wFt4XrgMvnvhAhgJbHK1iAE0xnqp1VcllNdnR7dw6QwMBn53La9wrfNJItIO+BDIAJQUkerAMGNMe3uTJU5EBmN1sSiP1Y0hEOtm6G47c93EF1gvjjfDmin0X2As1oRVvmoGEA5sxOr+5Ov6AH8ApUVkFZAXeMjeSOp2ot1a0ohrdr9KwP+wZh6LkR1rivlKtgRTtnPNUtgX2I6fzFIIsUPn1XUt/mOMOWlnnpvxt7z+RkQ2YFUYl7l1BfDp0VBEZDNQA9joltmnu+2Ja0bhOF0uthhjqtmdLTHi47NrJkREArBu2gTYE9MlTqm0oC3naac8cD/WiAvt3NZfBJ6xJVEy+Nu47H7qjDFmlt0hkkNE7nTNVBnz9OeY6+9CIlLIGLPRrmxJEZFhrmHbZrqWHSLyvTGmi83REiUis4g/0sx5rJdcxxtjfK0FMtIYcz7OrIq+3vpz3TWraUz3hSx2B0qGCNfQlTGZ8+J2U++j/haRKsaYbXYHSQ7Xda8X1gvDBvhLRL70wd85lU5p5TyNGGNmAjNFpL4xZrXg0NGJAAAKoklEQVTdeVJgKta47K1wG5fd1kTpjz/NUvg61s1kQjPPGayWU19UTEQGGGNGuMbu/wXrEbsvO4j1OH2aa/lRrLHwywETgW425UrMdhF5HHCKSFmgN/C3zZlu5mcRGQ/kFJFngJ5Y+9aXjcHq6pRPRN7H6m7xjr2REiYi27DOCwHAkyJyEP8YFedbrIazz1zLnbGuhQ/blkjdVrRbSxoTkf8B7wFXgPlANeBVY8x3tgZLhD+Py+4v/HGWQn/jeonue6xZFZsC84wxH9ubKmkissIY0yihdSKyw9e6wolIZqyXxltiVb4WAO/6cmujiIwCFuOZubkx5g1bg92Eq5vkvViZlxhjfLLBJLHRcGL4ate9hLoJ+XrXIZW+aOU8jcUMxyQiDwAdgdeApb76Sy8ia40xdURkBdZjvpPAWl+ddtkf+Xq/XHci8mBS232ttT/O+MqBwHhgFa7xin21Gw6AiOwCWhljjrqWiwHzjTEVRaflThUx/bfjrPP1PudTjTHdbrZO/XciMhn40hizxrVcF+hhjOllazB129BuLWkvZqi8NsA0Y0xonD6avmaCiOTCemz6B9YwYz495bIfWiMiFf1kwoh2SWwzgE9Vzonf/SYMazKij/DtbjhgdSFaKSIHsFpISwK9XP2ifW7scBEph/Vicwncri2++JRNRF7AamwoJSLuMx5nw7p582UeT0xcLy766mhf/qou0F1EjrqWiwG7Yrrp+PLNm0oftOU8jYk19XJHrG4tdbBeEJ1tjKmb5AdVuuVqIS0N+NMshSoNuPrH34l1TOz28S4iW4AvsWZejYpZb4zxuZlYRSQHkAsYAbzptumiMSY04U/ZS0QGYA25mgm47LYpAphgjBlgS7B0yF+746j0QyvnNnC1RF8wxkS5+mlm99Vh3UQkPzAcKGSMaS0iFYH6xhifncbY3/jjLIWuys1gIKZP9HKsMa19cqIOfz2ORaQB8Vuiv7UtUBIkgRlNVeoTkRFYQ/KW48YIWsYYs8K+VOmLiJQGjhtjrolIE6Aq8K0xJtzeZOp2oZVzG/jZBXce1uQcA40x1VyPUDf5Sx9p5R0iMh1rXPaY7hXdgGrGmCT7pNvFH49jEZmK9URlMzdaoo0xprd9qeITkSDXl72B01gjibiPOuSTLdH+yjWqTG+gCNaxUQ9Y7Yvdh/yVa/z72ljX6QVYXTrLG2Pa2JlL3T60z3kaS+yCizV0ky/KY4z52fVIFWNMpIhE3exDKt0rbYzp5LY81HVB81X+eBzXBioa329B2YB1Dot5ecZ9kjUD6Mvjqas31myga4wxTV0jt/j6bLf+Jtp1jngQ+MQY85mIbLI7lLp9aOU87fnLBTfGJRHJzY0JL+phTYSibm9XRKShMWYlgIjcjfUeha/yx+N4O1AACLE7SFKMMSXBmrglbp9412QuKnVdNcZcFRFE5A7XpGDl7Q6VzkSISGegOzdegg9MorxSqUor52nPLy64bvpgPdIrLSKrsCZFecjeSMoHvABMcfU9B2sUlB425rkZfzyO8wA7RWQtnt1E2tsXKUl/AzWTsU7dmuMikhOYASwSkTAg2OZM6c2TwPPA+8aYQyJSEvDJuUhU+qR9ztOYiCwFqgN+ccEVkYex+twVBTphDTH1ji+PD628zzWKyENYXbRyYrVCG2PMMFuDJcHVz7w8VveLPcaYCJsjJUlEGie03hizPK2zJEVECgCFsSovj3Oje0t2rLGi77QrW3rnOkZyYI1/f93uPEqp1KEt52lviN0BUugdY8wvrhFmmmONDz0Oq5Kubl8zgXBgI3DC5iw35RoVqQ9Q3BjzjIiUFZHyxpjZdmdLjK9VwpPQCngC6wXF0W7rL2IN/ae8xI+OEb8QM455Ytt1eFuVVrTlXCUpZiZC1/Bd24wxP+jshEpEthtjKtudI7lE5CesFxe7G2Mqi0gmrBEuqtscLR4RWWmMaSgiF/GsKMSMf5/dpmhJEpFOxpjpdudQ6r9yG9b2RdffU11/dwEu+/KTQZW+aOU8jfjxBXc2Vstoc6xZ6K4Aa40x1WwNpmwlIhOAz4wx2+zOkhwist4YU9v9xlJEtuhxnLpEpC3WDJaxL4JqhUb5GxFZZYy5+2brlPIWh90BbhfGmIauv7MZY7K7/cnmqxVzl0ew+pzf55qAIQjPodLUbUREtrmmO28IbBSRPSKy1W29r7ruai2PGa2lNG7vfPgiEXkqgXUj7ciSHCLyJfAo8DJWo8PDQJIzLSrlo7KISMOYBdfcJFlszKNuM9pyrpRKNn+d1lpEWgBvAxWBhcDdwBPGmGV25kqKa+Kk74wx37uWvwAyGmN62pssYSKy1RhT1e3vrMBvxpiWdmdTKiVEpBbwDdbLtmC9X9NTB0JQaUUr50qpdM81+dc2rG5ZB4F/jDFn7U2VNFdL/x9YlYTWQKgx5lV7UyVORP4xxtQVkTXAg8A5YLsxpqzN0ZT6T0QkO1Y9ydfnRFDpjI7WopS6HUzC6orTAmvGys0issIY86m9seITkSC3xaexxrNeBQwTkSBjTKg9yW5qtmv87Q+wRvExwFf2RlIq5VxDxXYCSgABItbooPr+hEor2nKulLotiIgTa9rzplgTjFzxxTG4ReQQ8V8aj2GMMaXSOFKKuSo3GbXFUfkjEZmPNXfDBiAqZr0x5iPbQqnbilbOlVLpnogswXqhazXwF7DSGHPa3lSJExEHUN8Ys8ruLMnlGkv+daBYzFjygE+PJa9UQvxtqFiV/uhoLUqp28FW4DpQGagKxIx17pOMMdHAh3bnSKFJWCPg1HctHwfesy+OUv/Z3yJSxe4Q6valLedKqduGawSRJ4G+QAFjzB02R0qUiAzFuqn4zfjBiVrHklfphYjsBMoAh7BuOGPmI/l/e3eoYlUUhQH4X7YJ+ga+gcFiVMQmiN13MAmCJpsIwvgGNt/AYJLBQUw6YBJMmmyKhkFQtuGcgQnXO45h9j5zv6/dm1Zc67DWv70QyolwEAqcelV1O8nlTA9pfcqUgLLbtaij3cm0ivO7qvYz+INlWWCWPPzF9d4FsNk058Am2EqyneRta+1X72L+RWvtbO8ajulBkhdJzlfVs8xZ8l0rgmOoqnOtte9JfvSuhc1mrQVgUFV1M8mV+efOyMeVS8ySh8Oq6nlr7cahxKTFJSVxOmjOAQZUVY8yRT8+m/+6lenL/71+Vf1dVV3LlCV/OXOWfJIhs+RhnXnQfJVkt7X2oXc9bB7NOcCAqup9kotzcstBTvveyEdpS8mSh3VWDJp7mRp1gyYnQnMOMKC5Ob968CLo/HLozqjN+dKy5GEdgyY9OQgFGNPDJO+qaifT7uuVJPe7VrTe+0xpOBcyva74raretNb2+5YFx7Ni0Lxk0OQk+XIOMKB57/Vjkq9JPmc6sPzSt6qjLSlLHlapqieZBs2fSV5n2j83aHJiNOcAA1rageWKLPmDg7qXXQuD/2TQpBfNOcCglrT3WlV3MzXki8mSh1UMmvSmOQcYkANL6MOgSW8OQgHG5MASOmitPe5dA5vNl3OAgdl7BdgsvpwDDGjF3uvTTOstAJximnOAMW0l2Y69V4CNYq0FAAAGcaZ3AQAAwERzDgAAg9CcAwDAIDTnAAAwCM05AAAM4g9xQpu7iLLGpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb042b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=data.columns \n",
    "data_corr = data.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('house_coor.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "holiday,weekday,workingday都与cnt相关性过低，所以数据丢弃这几个数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant  season  yr  mnth  weathersit      temp     atemp       hum  \\\n",
       "0        1       1   0     1           2  0.344167  0.363625  0.805833   \n",
       "1        2       1   0     1           2  0.363478  0.353739  0.696087   \n",
       "2        3       1   0     1           1  0.196364  0.189405  0.437273   \n",
       "3        4       1   0     1           1  0.200000  0.212122  0.590435   \n",
       "4        5       1   0     1           1  0.226957  0.229270  0.436957   \n",
       "\n",
       "   windspeed   cnt  \n",
       "0   0.160446   985  \n",
       "1   0.248539   801  \n",
       "2   0.248309  1349  \n",
       "3   0.160296  1562  \n",
       "4   0.186900  1600  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(\"holiday\", axis=1).drop(\"weekday\", axis=1).drop(\"workingday\", axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.6 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = data.drop(\"cnt\", axis=1).columns\n",
    "\n",
    "train_data = data[data.yr == 0]\n",
    "test_data = data[data.yr == 1]\n",
    "\n",
    "\n",
    "y_train = train_data['cnt'].values\n",
    "X_train = train_data.drop('cnt', axis = 1)\n",
    "\n",
    "y_test = test_data['cnt'].values\n",
    "X_test = test_data.drop('cnt', axis = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.7 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "#y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "#y_test = ss_y.transform(y_test.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、模型训练与模型评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6190256408184165\n",
      "The r2 score of RidgeCV on train is 0.7551016399378379\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "w = y_test.mean() - y_train.mean()\n",
    "y_test_pred_ridge = ridge.predict(X_test) + w\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe433b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013.394900</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1679.205620</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1081.160257</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>284.715628</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-268.294847</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-482.061572</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-909.006402</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1064.640197</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_ridge     columns\n",
       "5  2013.394900        temp\n",
       "6  1679.205620       atemp\n",
       "1  1081.160257      season\n",
       "3   284.715628        mnth\n",
       "2     0.000000          yr\n",
       "0  -268.294847     instant\n",
       "7  -482.061572         hum\n",
       "8  -909.006402   windspeed\n",
       "4 -1064.640197  weathersit"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//nimTp3KEUccwSGHHMIJJ5zQeGP2Q9S+P2NwtLW1ZWePP0nDj0fS3Vu5ciWHH374UJehAdDVaxsRyzKz18+DeU1akqRCGdKSJBXKkJakQgzm5UcNjv6+poa0JBVg9OjRPPPMMwb1LqTz+6RHjx7d523Yu1uSCjBhwgTWrFlDR0fHUJeiJho9ejQTJkzo8/qGtCQVoKWlhUmTJg11GSqMp7slSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUL2GdESMjogfRsSPIuLhiPiTavqkiFgSEasi4taIeN3AlytJ0vDRyJH0y8A7M/MYYApwWkQcD3we+GJmvgn4T2DuwJUpSdLw02tIZ83G6mFLdUvgncAd1fQbgLMGpEJJkoapkY0sFBEjgGXAG4H5wM+AX2Xm5mqRNcBB3ax7MXAxwMEHH9zfeiUNkvb25i4nacc11HEsM7dk5hRgAjANOLyrxbpZd0FmtmVmW2tra98rlSRpmNmh3t2Z+StgEXA8sE9EdB6JTwB+2dzSJEka3hrp3d0aEftU93cHTgZWAvcD760WmwPcNVBFSpI0HDVyTXo8cEN1XXo34LbM/HZE/AS4JSI+A/w78NUBrFOSpGGn15DOzBXA1C6mP0bt+rQkSRoAjjgmSVKhDGlJkgplSEuSVKiGBjORpBI54Ip2dR5JS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklSokUNdgKTB1d4+1BVIapRH0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCOZiJpOI44IpU45G0JEmFMqQlSSqUIS1JUqEMaUmSCtVrSEfEGyLi/ohYGREPR8Sl1fT2iFgbEcur23sGvlxJkoaPRnp3bwb+MDMfjIg9gWURcV8174uZec3AlSdJ0vDVa0hn5jpgXXV/Q0SsBA4a6MIkSRruduiadERMBKYCS6pJl0TEioi4LiL2bXJtkiQNaw2HdESMAb4JfCwznwe+DPwOMIXakfZfdLPexRGxNCKWdnR0NKFkSZKGh4ZCOiJaqAX0zZn59wCZuT4zt2Tmq8DfAtO6WjczF2RmW2a2tba2NqtuSZJ2eY307g7gq8DKzPxC3fTxdYudDTzU/PIkSRq+GundfQJwHvDjiFheTfsU8MGImAIk8Djw4QGpUJKkYaqR3t0PANHFrH9sfjmSJKmTI45JklQoQ1qSpEIZ0pIkFaqRjmOStFNrbx+YZaWB5pG0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVC9hnREvCEi7o+IlRHxcERcWk0fGxH3RcSq6ue+A1+uJEnDRyNH0puBP8zMw4HjgY9GxGTgMmBhZr4JWFg9liRJTdJrSGfmusx8sLq/AVgJHATMBm6oFrsBOGugipQkaTjaoWvSETERmAosAQ7IzHVQC3JgXDfrXBwRSyNiaUdHR/+qlSRpGGk4pCNiDPBN4GOZ+Xyj62Xmgsxsy8y21tbWvtQoSdKw1FBIR0QLtYC+OTP/vpq8PiLGV/PHA08NTImSJA1PjfTuDuCrwMrM/ELdrLuBOdX9OcBdzS9PkqTha2QDy5wAnAf8OCKWV9M+BXwOuC0i5gJPAu8bmBIlSRqeeg3pzHwAiG5mz2xuOZIkqZMjjkmSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUI18C5akIdLe3tzl1DvbXCXxSFqSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKAczkXYBQzmwhoN6SAPHI2lJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUL2GdERcFxFPRcRDddPaI2JtRCyvbu8Z2DIlSRp+GjmSvh44rYvpX8zMKdXtH5tbliRJ6jWkM3Mx8Owg1CJJkur055r0JRGxojodvm93C0XExRGxNCKWdnR09GN3kiQNL30N6S8DvwNMAdYBf9Hdgpm5IDPbMrOttbW1j7uTJGn46VNIZ+b6zNySma8CfwtMa25ZkiSpTyEdEePrHp4NPNTdspIkqW9G9rZARHwDmAHsHxFrgCuBGRExBUjgceDDA1ijJEnDUq8hnZkf7GLyVwegFkmSVMcRxyRJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQo0c6gKkXUl7e3OXU7l8rTUYPJKWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcrBTDRsORiFVOPvQrk8kpYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgrVa0hHxHUR8VREPFQ3bWxE3BcRq6qf+w5smZIkDT+NHElfD5y23bTLgIWZ+SZgYfVYkiQ1Ua8hnZmLgWe3mzwbuKG6fwNwVpPrkiRp2OvrNekDMnMdQPVzXHcLRsTFEbE0IpZ2dHT0cXeSJA0/A95xLDMXZGZbZra1trYO9O4kSdpl9DWk10fEeIDq51PNK0mSJEHfQ/puYE51fw5wV3PKkSRJnRr5CNY3gH8BDouINRExF/gc8K6IWAW8q3osSZKaqNdvwcrMD3Yza2aTa5EkSXUccUySpEIZ0pIkFcqQliSpUL1ek5Yk9V17+86xTZXJI2lJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoRzMROqFg1GoNI2+f3yf7fw8kpYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFWrkUBcgNZtfdC9pV+GRtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVKh+9e6OiMeBDcAWYHNmtjWjKEmS1JyPYL0jM59uwnYkSVIdT3dLklSo/h5JJ3BvRCTwN5m5YPsFIuJi4GKAgw8+uJ+703DmICXSjvF3ZufX3yPpEzLzWODdwEcj4qTtF8jMBZnZlpltra2t/dydJEnDR79COjN/Wf18CrgTmNaMoiRJUj9COiL2iIg9O+8DpwAPNaswSZKGu/5ckz4AuDMiOrfz9cz8p6ZUJUmS+h7SmfkYcEwTa5EkSXX8CJYkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCtWfb8GS+q29fagrkDQQGv3d9m9AzzySliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKwUy2syt9AH9Hamz289kZ2keSSueRtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQkZmDtrO2trZcunRp07Y3lANmDNd9S1LJBmIQp4H4mxsRyzKzrbflPJKWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIK1a+QjojTIuLRiFgdEZc1qyhJktSPkI6IEcB84N3AZOCDETG5WYVJkjTc9edIehqwOjMfy8xXgFuA2c0pS5Ik9Xkwk4h4L3BaZl5YPT4PeGtmXrLdchcDF1cPDwMe7Xu5w8L+wNNDXcQwYVsPHtt68NjWg6c/bf3bmdna20Ij+7hxgOhi2msSPzMXAAv6sZ9hJSKWNjIKjfrPth48tvXgsa0Hz2C0dX9Od68B3lD3eALwy/6VI0mSOvUnpP8NeFNETIqI1wEfAO5uTlmSJKnPp7szc3NEXAJ8FxgBXJeZDzetsuHLSwODx7YePLb14LGtB8+At/WgfguWJElqnCOOSZJUKENakqRCGdKDKCKujohHImJFRNwZEfvUzbu8Gl710Yg4tW56l0OvVh32lkTEqoi4teq8p0pEvC8iHo6IVyOibbt5tvUgcejg5oiI6yLiqYh4qG7a2Ii4r3pf3hcR+1bTIyKurdp8RUQcW7fOnGr5VRExZyieS8ki4g0RcX9ErKz+flxaTR+6ts5Mb4N0A04BRlb3Pw98vro/GfgRMAqYBPyMWme8EdX9Q4DXVctMrta5DfhAdf+vgXlD/fxKugGHUxs8ZxHQVjfdth6816DbNvW2w215EnAs8FDdtP8NXFbdv6zu78l7gHuojWVxPLCkmj4WeKz6uW91f9+hfm4l3YDxwLHV/T2Bn1Z/M4asrT2SHkSZeW9mbq4e/iu1z5ZDbTjVWzLz5cz8ObCa2rCrXQ69GhEBvBO4o1r/BuCswXoeO4PMXJmZXY1uZ1sPHocObpLMXAw8u93k2dTej7Dt+3I2cGPW/CuwT0SMB04F7svMZzPzP4H7gNMGvvqdR2auy8wHq/sbgJXAQQxhWxvSQ+cCav+BQe1N8Iu6eWuqad1N3w/4VV3gd05X72zrwdNdm6o5DsjMdVALF2BcNX1H3+PqQkRMBKYCSxjCtu7PsKDqQkR8D3h9F7OuyMy7qmWuADYDN3eu1sXySdf/RGUPyw8rjbR1V6t1Mc22Hhi23dDort19PRoUEWOAbwIfy8znayfUul60i2lNbWtDusky8+Se5lcdCE4HZmZ18YKeh1jtavrT1E6rjKyO8IblkKy9tXU3bOvB49DBA2t9RIzPzHXVKdanqundtfsaYMZ20xcNQp07lYhooRbQN2fm31eTh6ytPd09iCLiNOCTwJmZ+eu6WXcDH4iIURExCXgT8EO6GXq1Cvf7gfdW688Bujty1LZs68Hj0MED625q70fY9n15N3B+1fP4eOC56hTtd4FTImLfqnfyKdU0Vao+KF8FVmbmF+pmDV1bD3VvuuF0o9ZJ6RfA8ur213XzrqDWE/ZR4N11099DrYfhz6idxu2cfgi1cFkN3A6MGurnV9INOJvaf7MvA+uB79rWQ/I6dNmm3na4Hb8BrAM2Ve/rudT6SywEVlU/x1bLBjC/avMfs+2nGy6o3sergf8+1M+rtBvwNmqnpVfU/Z1+z1C2tcOCSpJUKE93S5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKh/j9vDaTqiWRaxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_ridge,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Train Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd188780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_test - y_test_pred_ridge,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Test Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb31d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察训练数据预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_ridge)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('Train True price')\n",
    "plt.ylabel('Train Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba54588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察测试数据预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_test, y_test_pred_ridge)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('Test True price')\n",
    "plt.ylabel('Test Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.6063341530753421\n",
      "The r2 score of LassoCV on train is 0.7503605496966871\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda2\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "lasso = LassoCV(alphas=alphas)  \n",
    "#lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "w = y_test.mean() - y_train.mean()\n",
    "y_test_pred_lasso = lasso.predict(X_test) + w\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b205f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.01)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.506093</td>\n",
       "      <td>[0.358766019237162]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.29921696719374713]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.167149</td>\n",
       "      <td>[0.1926515069653044]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0507333621195803]</td>\n",
       "      <td>mnth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-0.047807349831509055]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.085898355674672]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.16197548138846932]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.119779</td>\n",
       "      <td>[-0.18970780424953002]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coef_lasso               coef_ridge     columns\n",
       "5    0.506093      [0.358766019237162]        temp\n",
       "6    0.000000    [0.29921696719374713]       atemp\n",
       "1    0.167149     [0.1926515069653044]      season\n",
       "3    0.000000     [0.0507333621195803]        mnth\n",
       "2    0.000000                    [0.0]          yr\n",
       "0    0.000000  [-0.047807349831509055]     instant\n",
       "7   -0.000000     [-0.085898355674672]         hum\n",
       "8   -0.000000   [-0.16197548138846932]   windspeed\n",
       "4   -0.119779   [-0.18970780424953002]  weathersit"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、岭回归 完整代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.6190256408184165\n",
      "The r2 score of RidgeCV on train is 0.7551016399378379\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbb33e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 1.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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//nimTp3KEUccwSGHHMIJJ5zQeGP2Q9S+P2NwtLW1ZWePP0nDj0fS3Vu5ciWHH374UJehAdDVaxsRyzKz18+DeU1akqRCGdKSJBXKkJakQgzm5UcNjv6+poa0JBVg9OjRPPPMMwb1LqTz+6RHjx7d523Yu1uSCjBhwgTWrFlDR0fHUJeiJho9ejQTJkzo8/qGtCQVoKWlhUmTJg11GSqMp7slSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUL2GdESMjogfRsSPIuLhiPiTavqkiFgSEasi4taIeN3AlytJ0vDRyJH0y8A7M/MYYApwWkQcD3we+GJmvgn4T2DuwJUpSdLw02tIZ83G6mFLdUvgncAd1fQbgLMGpEJJkoapkY0sFBEjgGXAG4H5wM+AX2Xm5mqRNcBB3ax7MXAxwMEHH9zfeiUNkvb25i4nacc11HEsM7dk5hRgAjANOLyrxbpZd0FmtmVmW2tra98rlSRpmNmh3t2Z+StgEXA8sE9EdB6JTwB+2dzSJEka3hrp3d0aEftU93cHTgZWAvcD760WmwPcNVBFSpI0HDVyTXo8cEN1XXo34LbM/HZE/AS4JSI+A/w78NUBrFOSpGGn15DOzBXA1C6mP0bt+rQkSRoAjjgmSVKhDGlJkgplSEuSVKiGBjORpBI54Ip2dR5JS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklSokUNdgKTB1d4+1BVIapRH0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCOZiJpOI44IpU45G0JEmFMqQlSSqUIS1JUqEMaUmSCtVrSEfEGyLi/ohYGREPR8Sl1fT2iFgbEcur23sGvlxJkoaPRnp3bwb+MDMfjIg9gWURcV8174uZec3AlSdJ0vDVa0hn5jpgXXV/Q0SsBA4a6MIkSRruduiadERMBKYCS6pJl0TEioi4LiL2bXJtkiQNaw2HdESMAb4JfCwznwe+DPwOMIXakfZfdLPexRGxNCKWdnR0NKFkSZKGh4ZCOiJaqAX0zZn59wCZuT4zt2Tmq8DfAtO6WjczF2RmW2a2tba2NqtuSZJ2eY307g7gq8DKzPxC3fTxdYudDTzU/PIkSRq+GundfQJwHvDjiFheTfsU8MGImAIk8Djw4QGpUJKkYaqR3t0PANHFrH9sfjmSJKmTI45JklQoQ1qSpEIZ0pIkFaqRjmOStFNrbx+YZaWB5pG0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVC9hnREvCEi7o+IlRHxcERcWk0fGxH3RcSq6ue+A1+uJEnDRyNH0puBP8zMw4HjgY9GxGTgMmBhZr4JWFg9liRJTdJrSGfmusx8sLq/AVgJHATMBm6oFrsBOGugipQkaTjaoWvSETERmAosAQ7IzHVQC3JgXDfrXBwRSyNiaUdHR/+qlSRpGGk4pCNiDPBN4GOZ+Xyj62Xmgsxsy8y21tbWvtQoSdKw1FBIR0QLtYC+OTP/vpq8PiLGV/PHA08NTImSJA1PjfTuDuCrwMrM/ELdrLuBOdX9OcBdzS9PkqTha2QDy5wAnAf8OCKWV9M+BXwOuC0i5gJPAu8bmBIlSRqeeg3pzHwAiG5mz2xuOZIkqZMjjkmSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUI18C5akIdLe3tzl1DvbXCXxSFqSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKAczkXYBQzmwhoN6SAPHI2lJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUL2GdERcFxFPRcRDddPaI2JtRCyvbu8Z2DIlSRp+GjmSvh44rYvpX8zMKdXtH5tbliRJ6jWkM3Mx8Owg1CJJkur055r0JRGxojodvm93C0XExRGxNCKWdnR09GN3kiQNL30N6S8DvwNMAdYBf9Hdgpm5IDPbMrOttbW1j7uTJGn46VNIZ+b6zNySma8CfwtMa25ZkiSpTyEdEePrHp4NPNTdspIkqW9G9rZARHwDmAHsHxFrgCuBGRExBUjgceDDA1ijJEnDUq8hnZkf7GLyVwegFkmSVMcRxyRJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQo0c6gKkXUl7e3OXU7l8rTUYPJKWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcrBTDRsORiFVOPvQrk8kpYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgrVa0hHxHUR8VREPFQ3bWxE3BcRq6qf+w5smZIkDT+NHElfD5y23bTLgIWZ+SZgYfVYkiQ1Ua8hnZmLgWe3mzwbuKG6fwNwVpPrkiRp2OvrNekDMnMdQPVzXHcLRsTFEbE0IpZ2dHT0cXeSJA0/A95xLDMXZGZbZra1trYO9O4kSdpl9DWk10fEeIDq51PNK0mSJEHfQ/puYE51fw5wV3PKkSRJnRr5CNY3gH8BDouINRExF/gc8K6IWAW8q3osSZKaqNdvwcrMD3Yza2aTa5EkSXUccUySpEIZ0pIkFcqQliSpUL1ek5Yk9V17+86xTZXJI2lJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoRzMROqFg1GoNI2+f3yf7fw8kpYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFWrkUBcgNZtfdC9pV+GRtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVKh+9e6OiMeBDcAWYHNmtjWjKEmS1JyPYL0jM59uwnYkSVIdT3dLklSo/h5JJ3BvRCTwN5m5YPsFIuJi4GKAgw8+uJ+703DmICXSjvF3ZufX3yPpEzLzWODdwEcj4qTtF8jMBZnZlpltra2t/dydJEnDR79COjN/Wf18CrgTmNaMoiRJUj9COiL2iIg9O+8DpwAPNaswSZKGu/5ckz4AuDMiOrfz9cz8p6ZUJUmS+h7SmfkYcEwTa5EkSXX8CJYkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCtWfb8GS+q29fagrkDQQGv3d9m9AzzySliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKwUy2syt9AH9Hamz289kZ2keSSueRtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQkZmDtrO2trZcunRp07Y3lANmDNd9S1LJBmIQp4H4mxsRyzKzrbflPJKWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIK1a+QjojTIuLRiFgdEZc1qyhJktSPkI6IEcB84N3AZOCDETG5WYVJkjTc9edIehqwOjMfy8xXgFuA2c0pS5Ik9Xkwk4h4L3BaZl5YPT4PeGtmXrLdchcDF1cPDwMe7Xu5w8L+wNNDXcQwYVsPHtt68NjWg6c/bf3bmdna20Ij+7hxgOhi2msSPzMXAAv6sZ9hJSKWNjIKjfrPth48tvXgsa0Hz2C0dX9Od68B3lD3eALwy/6VI0mSOvUnpP8NeFNETIqI1wEfAO5uTlmSJKnPp7szc3NEXAJ8FxgBXJeZDzetsuHLSwODx7YePLb14LGtB8+At/WgfguWJElqnCOOSZJUKENakqRCGdKDKCKujohHImJFRNwZEfvUzbu8Gl710Yg4tW56l0OvVh32lkTEqoi4teq8p0pEvC8iHo6IVyOibbt5tvUgcejg5oiI6yLiqYh4qG7a2Ii4r3pf3hcR+1bTIyKurdp8RUQcW7fOnGr5VRExZyieS8ki4g0RcX9ErKz+flxaTR+6ts5Mb4N0A04BRlb3Pw98vro/GfgRMAqYBPyMWme8EdX9Q4DXVctMrta5DfhAdf+vgXlD/fxKugGHUxs8ZxHQVjfdth6816DbNvW2w215EnAs8FDdtP8NXFbdv6zu78l7gHuojWVxPLCkmj4WeKz6uW91f9+hfm4l3YDxwLHV/T2Bn1Z/M4asrT2SHkSZeW9mbq4e/iu1z5ZDbTjVWzLz5cz8ObCa2rCrXQ69GhEBvBO4o1r/BuCswXoeO4PMXJmZXY1uZ1sPHocObpLMXAw8u93k2dTej7Dt+3I2cGPW/CuwT0SMB04F7svMZzPzP4H7gNMGvvqdR2auy8wHq/sbgJXAQQxhWxvSQ+cCav+BQe1N8Iu6eWuqad1N3w/4VV3gd05X72zrwdNdm6o5DsjMdVALF2BcNX1H3+PqQkRMBKYCSxjCtu7PsKDqQkR8D3h9F7OuyMy7qmWuADYDN3eu1sXySdf/RGUPyw8rjbR1V6t1Mc22Hhi23dDort19PRoUEWOAbwIfy8znayfUul60i2lNbWtDusky8+Se5lcdCE4HZmZ18YKeh1jtavrT1E6rjKyO8IblkKy9tXU3bOvB49DBA2t9RIzPzHXVKdanqundtfsaYMZ20xcNQp07lYhooRbQN2fm31eTh6ytPd09iCLiNOCTwJmZ+eu6WXcDH4iIURExCXgT8EO6GXq1Cvf7gfdW688Bujty1LZs68Hj0MED625q70fY9n15N3B+1fP4eOC56hTtd4FTImLfqnfyKdU0Vao+KF8FVmbmF+pmDV1bD3VvuuF0o9ZJ6RfA8ur213XzrqDWE/ZR4N11099DrYfhz6idxu2cfgi1cFkN3A6MGurnV9INOJvaf7MvA+uB79rWQ/I6dNmm3na4Hb8BrAM2Ve/rudT6SywEVlU/x1bLBjC/avMfs+2nGy6o3sergf8+1M+rtBvwNmqnpVfU/Z1+z1C2tcOCSpJUKE93S5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKh/j9vDaTqiWRaxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Jam7ixIksW7aMVatW9Xcp6kUjR45k4sSJ3X58pyGd1ce69n870lLfEngd8Fd1+9VAG4a0JHVLS0sLkydP7u8yVJgunZOOiGERsRB4DLgN+BXwh8xcXy+yDJjQwWPPioj5ETHfT4iSJHVdl0I6Mzdk5hRgIjANaPZlvqYnUjJzTma2ZmZr+/itkiSpc9t1dXdm/gG4AzgS2C0i2g+XTwR+27ulSZI0tHXl6u6xEbFbPf1C4PXAYuB24C/rxU4Dtv6nn5Ikqdu6cnX3eODqiBhGFeo3ZOZ/RsQDwHUR8UngZ8AX+7BOSZKGnK5c3X0fsNV/6c7Mh6nOT0uSpD7giGOSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCDe/vAiSpu9raenc5qTTuSUuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqE6DemI2Dcibo+IxRFxf0ScV7e3RcTyiFhY397U9+VKkjR0DO/CMuuBD2XmPRGxC7AgIm6r530uMy/tu/IkSRq6Og3pzFwBrKin10TEYmBCXxcmSdJQ15U96U0iYhIwFbgLOAp4b0ScCsyn2tv+fZPHnAWcBbDffvv1sFxJA1lbW39XIA0sXb5wLCJGAV8H3p+ZTwJXAC8GplDtaX+22eMyc05mtmZm69ixY3uhZEmShoYuhXREtFAF9NzM/AZAZq7MzA2ZuRH4d2Ba35UpSdLQ05WruwP4IrA4M/+loX18w2JvARb1fnmSJA1dXTknfRRwCvDziFhYt10AvCMipgAJPAqc3ScVSpI0RHXl6u4fAtFk1nd6vxxJktTOEcckSSqUIS1JUqEMaUmSCmVIS5JUqO0acUzS0NHV0cEcRUzqO+5JS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChHHJOGmKE4Qlhf9HkoPo/a8dyTliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUa3t8FSBrY2tr6u4KydfX58XlUM+5JS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqVKchHRH7RsTtEbE4Iu6PiPPq9jERcVtEPFT/3L3vy5Ukaejoyp70euBDmfly4EjgPRFxIPAxYF5mvgSYV9+XJEm9pNOQzswVmXlPPb0GWAxMAGYDV9eLXQ2c2FdFSpI0FG3XiGMRMQmYCtwF7JWZK6AK8ogY18FjzgLOAthvv/16UqskFcMRwrQjdPnCsYgYBXwdeH9mPtnVx2XmnMxszczWsWPHdqdGSZKGpC6FdES0UAX03Mz8Rt28MiLG1/PHA4/1TYmSJA1NXbm6O4AvAosz818aZn0TOK2ePg24pffLkyRp6OrKOemjgFOAn0fEwrrtAuDTwA0RcQbwa+CtfVOiJElDU6chnZk/BKKD2TN7txxJktTOEcckSSqUIS1JUqEMaUmSCmVIS5JUqO0acUyS1P+6OtqZo6INfO5JS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgrlYCaSVAAHHlEz7klLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklSoTkM6Ir4UEY9FxKKGtraIWB4RC+vbm/q2TEmShp6u7ElfBRzXpP1zmTmlvn2nd8uSJEmdhnRm3gk8sQNqkSRJDXpyTvq9EXFffTh8916rSJIkAd0P6SuAFwNTgBXAZztaMCLOioj5ETF/1apV3dycJElDT7dCOjNXZuaGzNwI/DswbRvLzsnM1sxsHTt2bHfrlCRpyOlWSEfE+Ia7bwEWdbSsJEnqnuGdLRARXwNmAHtGxDLgImBGREwBEngUOLsPa5QkaUjqNKQz8x1Nmr/YB7VIkqQGjjgmSVKhDGlJkgplSEuSVChDWpKkQnV64Zik/tPW1t8VSOpP7klLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBXKkJYkqVCGtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBWq05COiC9FxGMRsaihbUxE3BYRD9U/d+/bMiVJGnq6sid9FXDcFm0fA+Zl5kuAefV9SZLUizoN6cy8E3hii+bZwNX19NXAib1clyRJQ153z0nvlZkrAOqf4zpaMCLOioj5ETF/1apV3dycJElDT59fOJaZczKzNTNbx44d29ebkyRp0OhuSK+MiPEA9c/Heq8kSZIE3Q/pbwKn1dOnAbf0TjmSJKldV76C9TXgx8BLI2JZRJwBfBp4Q0Q8BLyhvi9JknrR8M4WyMx3dDBrZi/XIkmSGjjimCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEJ1+q8qpYGmra13lxso25Y0+LgnLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEccUzqhKODabBzpLxyuSctSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmF6tH/k46IR4E1wAZgfWa29kZRkiSphyFde21mPt4L65EkSQ083C1JUqF6uiedwK0RkcD/y8w5Wy4QEWcBZwHst99+PdycJKmr2tr6uwL1VE/3pI/KzMOBNwLviYijt1wgM+dkZmtmto4dO7aHm5MkaejoUUhn5m/rn48BNwPTeqMoSZLUg5COiJ0jYpf2aeAYYFFvFSZJ0lDXk3PSewE3R0T7er6amf/VK1VJkqTuh3RmPgwc1ou1SJKkBn4FS5KkQhnSkiQVypCWJKlQhrQkSYXqjbG7JW0nR4KS1BXuSUuSVChDWpKkQhnSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIKZUhLklQoRxwT0PsjYPXFiFqO0iUNHF19v/q+3jb3pCVJKpQhLUlSoQxpSZIKZUhLklQoQ1qSpEIZ0pIkFcqQliSpUIa0JEmFcjAT9QkHKJAGH9/XO5570pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqEMaUmSCmVIS5JUKENakqRCGdKSJBVqQI84tj2j3/TnSDm9vW1H/ZE0WHT171lvL7c9+vNvrnvSkiQVypCWJKlQhrQkSYUypCVJKpQhLUlSoQxpSZIK1aOQjojjIuLBiFgSER/rraIkSVIPQjoihgGXA28EDgTeEREH9lZhkiQNdT3Zk54GLMnMhzPzOeA6YHbvlCVJkiIzu/fAiL8EjsvMd9f3TwFemZnv3WK5s4Cz6rsvBR7sfrk7zJ7A4/1dRB+xbwPPYO0XDN6+DdZ+gX3rLS/KzLGdLdSTYUGjSdtWiZ+Zc4A5PdjODhcR8zOztb/r6Av2beAZrP2Cwdu3wdovsG87Wk8Ody8D9m24PxH4bc/KkSRJ7XoS0j8FXhIRkyPiBcDbgW/2TlmSJKnbh7szc31EvBf4HjAM+FJm3t9rlfWvAXV4fjvZt4FnsPYLBm/fBmu/wL7tUN2+cEySJPUtRxyTJKlQhrQkSYUa0iEdER+OiIyIPev7ERGX1cOc3hcRhzcse1pEPFTfTmtof0VE/Lx+zGUR0eyraTtERPxjXffCiLg1Ivap2wd0v+p6LomIX9T13xwRuzXMO7+u88GIOLahvemwtfXFjnfVfb6+vvCx30TEWyPi/ojYGBGtW8wb0H3ryEAcUjgivhQRj0XEooa2MRFxW/183xYRu9ft2/2e6y8RsW9E3B4Ri+vfw/Pq9sHQt5ERcXdE3Fv37R/q9qbvk4gYUd9fUs+f1LCupu/FPpeZQ/JG9fWx7wFLgT3rtjcB36X6DviRwF11+xjg4frn7vX07vW8u4Hp9WO+C7yxH/u0a8P0+4ArB0O/6nqOAYbX0/8M/HM9fSBwLzACmAz8iupCxmH19P7AC+plDqwfcwPw9nr6SuCcfu7by6kG+rkDaG1oH/B966C/HdZf8g04GjgcWNTQ9hngY/X0xxp+L7f7PdeP/RoPHF5P7wL8sv7dGwx9C2BUPd0C3FXX3PR9ApzL83833w5cX083fS/uiD4M5T3pzwF/x+YDsMwGrsnKT4DdImI8cCxwW2Y+kZm/B24Djqvn7ZqZP87qlbwGOHHHduN5mflkw92deb5vA7pfAJl5a2aur+/+hOp7+VD17brMfDYzHwGWUA1Z23TY2vqIwOuAm+rHX03/921xZjYbiW/A960DA3JI4cy8E3hii+bZVM8zbP58b9d7ru+r71hmrsjMe+rpNcBiYAKDo2+ZmWvruy31Len4fdLY55uAmfX7qqP3Yp8bkiEdEScAyzPz3i1mTQB+03B/Wd22rfZlTdr7TUR8KiJ+A5wMfKJuHvD92sLpVJ/kYfv7tgfwh4bAL61vjQZr3zqqfyDaKzNXQBV2wLi6fXtfuyLUh3enUu1xDoq+RcSwiFgIPEb1weFXdPw+2dSHev4fqd5X/da3ngwLWrSI+D6wd5NZFwIXUB0+3ephTdqyG+19Zlv9ysxbMvNC4MKIOB94L3ARA6Bf0Hnf6mUuBNYDc9sf1mT5pPkH0KL71uxhTdqK61s3DJQ6e6Ko91ZXRMQo4OvA+zPzyej4MpQB1bfM3ABMqa9juZnq9NJWi9U/i+vboA3pzHx9s/aIOITqnMK99S/hROCeiJhGx0OdLgNmbNF+R90+scnyfaajfjXxVeDbVCFdfL+g877VF6IcD8ysD8PDtoenbdb+ONXhueH1J+Ui+taBAdG3bhhMQwqvjIjxmbmiPuT7WN2+ve+5fhURLVQBPTczv1E3D4q+tcvMP0TEHVTnpDt6n7T3bVlEDAdGU53i6L/f2R1x4rvkG/Aoz184NovNL4i4u24fAzxCdTHE7vX0mHreT+tl2y+welM/9uUlDdN/C9w0GPpV13Mc8AAwdov2g9j8go6HqS5MGl5PT+b5i5MOqh9zI5tfNHJuf/8e1rXcweYXjg2avm3Rzw7rL/0GTGLzC8cuYfOLqz5TT2/3e64f+xRU15386xbtg6FvY4Hd6ukXAj+g+qDf9H0CvIfNLxy7oZ5u+l7cIX3ozyewhBubh3QAl1Ods/j5Fn8wT6e6WGAJ8NcN7a3Aovoxn6cexa2f+vL1upb7gG8BEwZDv+p6llCdE1pY365smHdhXeeDNFyFTnUV6i/reRc2tO9PdfX6kvrNOqKf+/YWqk/qzwIrge8Nlr5to89N6y/5BnwNWAGsq1+vM6jOV84DHqp/tn/I3e73XD/261VUh27va3h/vWmQ9O1Q4Gd13xYBn6jbm75PgJH1/SX1/P0b1tX0vdjXN4cFlSSpUEPy6m5JkgYCQ1qSpEIZ0pIkFcqQliSpUIa0JEmFMqQlSSqUIS1JUqH+P6XySTk4HIshAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdab3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b15dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd13cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np  # 矩阵操作\n",
    "import pandas as pd # SQL数据处理\n",
    "\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "\n",
    "import matplotlib.pyplot as plt   #画图\n",
    "import seaborn as sns\n",
    "from numpy import array\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline\n",
    "\n",
    "data = pd.read_csv(\"day.csv\").drop(\"casual\", axis=1).drop(\"registered\", axis=1)\n",
    "\n",
    "data = data.drop(\"dteday\", axis=1)\n",
    "data = data.drop(\"holiday\", axis=1).drop(\"weekday\", axis=1).drop(\"workingday\", axis=1)\n",
    "\n",
    "\n",
    "\n",
    "columns = data.drop(\"cnt\", axis=1).columns\n",
    "\n",
    "train_data = data[data.yr == 0]\n",
    "test_data = data[data.yr == 1]\n",
    "\n",
    "y_train = train_data['cnt'].values\n",
    "X_train = train_data.drop('cnt', axis = 1)\n",
    "\n",
    "y_test = test_data['cnt'].values\n",
    "X_test = test_data.drop('cnt', axis = 1)\n",
    "\n",
    "\n",
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "#y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "#y_test = ss_y.transform(y_test.reshape(-1, 1))\n",
    "\n",
    "\n",
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "w = y_test.mean() - y_train.mean()\n",
    "y_test_pred_ridge = ridge.predict(X_test) + w\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge)\n",
    "\n",
    "\n",
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'],ascending=False)\n",
    "\n",
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_ridge,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Train Histogram of Residuals\") \n",
    "ax.legend(loc='best');\n",
    "\n",
    "#在测试集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_test - y_test_pred_ridge,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Test Histogram of Residuals\") \n",
    "ax.legend(loc='best');\n",
    "\n",
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_ridge)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('Train True price')\n",
    "plt.ylabel('Train Predicted price')\n",
    "plt.tight_layout()\n",
    "\n",
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_test, y_test_pred_ridge)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('Test True price')\n",
    "plt.ylabel('Test Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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